What Is Agentic AI — and Why Should Engineers Care?

Most AI tools in engineering today are passive: you ask a question or run an inference, you get an answer. Agentic AI is fundamentally different. An AI agent not only understands context (your CAD geometry, your simulation setup, your organization’s standards), it can take action too. Agents can spot and resolve problems before you encounter them, recommend and implement setups based on your geometry and what you want to do with it, and orchestrate entire workflows from setup to post-processing.

This matters because engineering simulation has always been bottlenecked by expertise. Geometry preparation, boundary condition and physics model selection, meshing… these tasks require deep knowledge and eat time. Agentic AI embeds that expertise directly into the workflow, making it accessible to every engineer regardless of their simulation experience.

And beyond providing assistance and guidance, AI agents have the capacity to run entire multi-step and even multi-tool workflows. These tasks have always been particularly challenging to automate using procedural programming, easily breaking down on edge cases where a single error can spell disaster. AI agents, on the other hand, thrive on that kind of ‘fuzzy’ decision making and error recovery that is needed, and that previously only a human operator could provide.

Five Agentic Workflows Already Reshaping Simulation

Here are five concrete ways agentic AI in engineering is changing how simulation gets done — some available today, others maturing fast.

1. Guided Onboarding for New Simulation Users

Making simulation accessible to new users has always been a core industry challenge. Engineering AI agents turbocharge this democratization. Rather than relying on static documentation, an agent understands your CAD model in real time, guides you step-by-step toward a viable setup, diagnoses missing inputs, and flags challenges before you hit “run.”

A mechanical engineer who has never run a CFD analysis can reach a working simulation in a fraction of the usual time. Onboarding transforms from a knowledge hurdle into a guided experience.

2. Intelligent Simulation Setup Automation

Setting up a simulation is often the most time-consuming phase — preparing CAD geometry, assigning boundary conditions, materials, and physics models. An agentic Engineering AI leverages geometric information and vast simulation knowledge to streamline this. The agent recognizes context from your geometry, recommends or auto-applies suitable settings, and raises issues only when human judgment is genuinely needed.

What once took hours of manual configuration can be reduced to minutes of guided collaboration with an AI agent. This pattern is emerging across the engineering software stack and particularly amongst modern cloud-native tools. PTC’s Onshape AI Advisor, for instance, now provides step-by-step recommendations and troubleshooting directly within the CAD environment, and CoLab Software’s AutoReview feature, which uses agentic automation to instantly verify that designs adhere to organizational standards and best practices before they ever reach a human reviewer.

When cloud-native engineering tools come together with easily accessible APIs, AI agents and MCP connections, the entire design-to-analysis loop becomes intelligent end to end. No clunky file transfers, no firewalls, no version incompatibilities.

3. Enforcing Organizational Best Practices at Scale

Scaling simulation quality across distributed teams remains a persistent pain point. Agentic AI addresses this by actively reinforcing organizational best practices throughout the workflow — surfacing internal guidelines, guarding against common pitfalls, and consolidating hard-won expertise.

Over time, these agents learn from recurring team mistakes and successes, driving continuous improvement. This is particularly valuable where knowledge transfer between experienced and junior analysts has traditionally been slow and inconsistent.

4. Multi-Agent Collaboration Across Engineering Tools

Some of the most transformative potential emerges when AI agents collaborate with each other — not just with humans. Multiple specialized agents, each with domain-specific reasoning, can work together across complex engineering challenges.

“SimScale allows us to embed simulation insight at the moment when decisions are being made, not after drawings are complete.”

Jon Wilde

VP of Product, SimScale

This kind of agent-to-agent orchestration is already taking shape. Violet Labs, which provides a cloud-based integration platform for hardware engineering, connects live data from requirements management, CAD, and analysis tools into a centralized engineering source of truth. Through its integration with SimScale, simulations can auto-execute whenever an input variable changes — and results feed back directly into the wider engineering stack. It is exactly this kind of connected, event-driven infrastructure that makes multi-agent workflows viable: when your data, requirements, and simulation tools all speak to each other, agents can act on changes without waiting for a human to initiate each step.

5. Automated RFQ Response Generation

In high-stakes engineering environments, agentic engineering transforms speed into a decisive competitive weapon. By leveraging engineering agents to compress the transition from an initial requirement to a physics-validated design from days into minutes, organizations can move from a reactive “best effort” stance to a proactive market position. This shift allows firms to gain an edge where it matters most: at the very start of the design cycle, before the competition has even finished their manual configuration.

A prime application of this strategic speed is the automation of RFQ (Request for Quotation) responses. Mature engineering agents can ingest customer requirements, map them to design specs, and run simulations to validate performance criteria—all automatically. This not only increases bid throughput without adding headcount but also allows teams to win contracts with greater confidence. Because these agents instantly identify cost and feasibility risks, firms can protect their margins while offering a level of technical certainty that manual workflows simply cannot match, effectively turning the engineering function into a primary engine for top-line growth.

The Execution Gap: Why 93% of Engineering Leaders Are Still Waiting

Despite the promise, the gap between expectation and reality is stark. The urgency is real — a recent CoLab survey of 250 engineering leaders found that 95% view AI adoption as essential over the next two years, with nearly half calling it a matter of survival. Yet SimScale’s State of Engineering AI 2025 survey reveals that while 93% of engineering leaders expect AI to deliver substantial productivity gains, only 3% report achieving transformational impact.

For this technology to help rather than hinder, the AI agents and models need to be so deeply and logically integrated into simulation tools that their use becomes second nature.”

Jon Wilde

VP of Product, SimScale

The root cause is infrastructure, not intent. Agentic AI cannot reason effectively over fragmented data stored across disconnected desktop tools. It requires a cloud-native environment where geometry, physics data, simulation history, and organizational knowledge live in a single connected platform.

What Could Go Wrong: Challenges to Watch

No technology shift is without risk, and agentic AI in engineering is no exception. Organizations adopting these workflows need to think carefully about validation and trust — how do you verify that an agent’s simulation setup is correct before committing compute resources? Human-in-the-loop checkpoints remain critical, especially for safety-critical applications in aerospace, automotive, and medical devices.

Data quality is another hurdle. Agents are only as good as the data and knowledge they can access. Organizations with fragmented CAD libraries, inconsistent naming conventions, and undocumented tribal knowledge will find that AI amplifies existing problems rather than solving them. Getting your digital house in order is a prerequisite, not a follow-up task.

Finally, there is the change management dimension. Engineers accustomed to full manual control may resist delegating decisions to an agent. Successful adoption requires demonstrating reliability incrementally — starting with low-risk workflows and expanding as trust builds.

The Winning Strategy: Engineering AI Meets Physics AI

A useful framework — outlined by CoLab, who are working with dozens of engineering leadership teams on agentic AI strategy — distinguishes three layers of AI in engineering: Prediction (Physics AI that uses deep learning surrogates to forecast outcomes in milliseconds), Automation (Engineering AI agents that orchestrate setup, meshing, and debugging), and Validation (Test AI that confirms real-world performance). When all three converge, the result is what CoLab and others are calling Generative Engineering — a mode where engineers define requirements and the system autonomously generates, evaluates, and validates design candidates.

In practice, leading organizations are starting with the first two layers. Engineering AI handles the process — setup, meshing, compliance checks, and workflow orchestration. Physics AI handles the predictions — reducing solve times from hours to seconds. When these merge, the result is a high-value loop: engineers provide design intent, and the system handles rigorous execution. Organizations like Convion (part of HD Hyundai) are deploying this combined approach today.

How to Get Started With Agentic AI in Engineering

The transition to agentic workflows is no longer a question of “if” but “how fast.” Here’s a practical starting point:

• Assess your readiness. Use SimScale’s AI Capability Index to benchmark your organization against the industry and identify where your infrastructure needs to evolve.
• Start small. Pick a single, repeatable simulation workflow — a routine thermal analysis, a standard valve CFD study — and pilot the AI agent on that. Build trust before expanding scope.
• Consolidate your data. AI agents need connected, well-organized data. Migrating to cloud-native simulation infrastructure is the highest-leverage investment you can make toward an agentic future.
• Learn from peers. SimScale’s Engineering AI Hub documents real-world case studies showing how industry leaders are already incorporating agentic AI into their engineering workflows.

The engineering teams that embrace this shift now will not just be more productive — they will be defining the standard for how engineering gets done in the age of AI.

The post Agentic AI in Engineering appeared first on SimScale.

From electric vehicles (EVs) and robotics to industrial machinery and HVAC systems, electric motors power critical infrastructure—and consume over 40% of global electricity.

The solution? Advanced electric motor design software powered by cloud-native simulation.

Modern motor design demands multiphysics optimization across electromagnetics, thermal management, and structural analysis.

Physical prototyping is too slow and expensive!

Simulation enables engineers to test, iterate, and optimize designs in hours instead of weeks, slashing development costs while maximizing efficiency and power density.

This guide covers the complete electric motor design workflow; from fundamental principles and motor types to the six-stage design process and multiphysics simulation techniques that deliver optimal performance.

Electric Motor Fundamentals: How They Work and Their Types

An electric motor converts electrical energy into mechanical motion through electromagnetism. When current flows through a wire coil in a magnetic field, it creates rotational force.

The motor consists of two main parts: the stator (stationary, houses windings that generate magnetic fields) and the rotor (rotating, produces mechanical output). Additional components include windings (copper or aluminum coils), and for DC motors, commutators and brushes that control current direction.

Types of Electric Motors

Electric motors are classified primarily by power source: DC (direct current) or AC (alternating current).

Comparative Motor Selection Guide

Choosing the right motor type depends on your specific application requirements:

Key Selection Factors:

• Efficiency priority: Synchronous PMSM > BLDC > AC Induction
• Cost sensitivity: Brushed DC or SRM > AC Induction > BLDC > PMSM
• Power-to-weight ratio (aerospace, EVs): PMSM > BLDC > SRM
• Harsh environments: SRM or AC Induction

Brushed vs Brushless: Performance Comparison

Performance: Brushless motors achieve 85-90% efficiency vs 75-80% for brushed, deliver 30-50% more power for the same size, and operate from 0 to 50,000+ RPM (vs ~10,000 RPM for brushed).

Operational: Brushed motors require brush replacement every 1,000-3,000 hours; brushless are maintenance-free with 10,000+ hour lifespans. Brushless motors run quieter but require electronic controllers (ESCs), adding cost and complexity.

Cost: Brushed motors cost 30-60% less initially, but brushless offer lower total cost of ownership due to longer life and higher efficiency.

When to Choose: Brushed for low-cost, simple applications; brushless when efficiency, continuous operation, and reliability are prioritized.

Explore detailed motor testing methodologies in our guide: How to Test an Electric Motor: Tools and Methods.

Electric Motor Design Process

The electric motor design process involves six critical stages:

1. Define Design Requirements

Establish clear specifications including performance targets (power, torque, speed, efficiency), operating conditions (temperature, duty cycle), size/weight constraints, and cost parameters. Energy consumed over a motor’s 20-year life typically represents 70-95% of total cost, making efficiency optimization critical.

2. Select Motor Type and Topology

Choose between AC, DC, synchronous, or induction motors based on application requirements. Decide on permanent magnet vs wound rotor configurations and determine number of poles and phases—decisions that directly impact torque, speed range, and control complexity.

3. Initial Design Calculations

Perform preliminary electromagnetic calculations to size the magnetic circuit and determine winding configurations. Calculate expected heat generation and select appropriate materials:

Key Material Choices:

• Magnetic cores: Silicon steel (M19 standard, M15 premium), amorphous metals for ultra-low losses
• Permanent magnets: NdFeB N35-N52 grades (higher = stronger but lower temp tolerance), SmCo for high-temperature applications (up to 350°C)
• Conductors: Copper (standard, 100% conductivity) or aluminum (40% lower cost, 61% conductivity)
• Insulation: Class 155-240 by temperature rating (155°C industrial to 240°C aerospace)

4. Detailed Design with Simulation

Simulation validates and refines designs through:

• Electromagnetic simulation: Visualize magnetic flux, calculate torque, identify saturation or losses
• Thermal simulation: Predict temperature distribution, design cooling systems
• Structural FEA: Ensure components withstand operational stresses and vibrations

5. Design Optimization

Conduct parametric studies varying geometry, materials, and winding patterns. Run multi-objective optimization balancing efficiency, power density, cost, and manufacturability. SimScale’s cloud platform enables unlimited parallel simulations, exploring entire design spaces in hours instead of weeks.

6. Prototype and Testing

Virtual prototyping through comprehensive multiphysics simulation predicts real-world performance across operating conditions. Physical prototypes validate predictions before full production.

“Developing a product like this from scratch requires a lot of simulation work across multiple physics to explore all of the possibilities in the design space. Getting speed, accuracy, usability, and cost efficiency in one package is hard to find anywhere else.”

Maximilian Güttinger

CEO & Co-founder, Emil Motors

Environmental Design Considerations

Extreme environments demand specialized design approaches:

High-Temperature (Aerospace, Industrial): Use SmCo magnets (vs NdFeB), Class 200+ insulation, enhanced cooling, ceramic bearings for operation above 150°C.

High-Vibration (Automotive, Construction): Strengthen rotor balancing, use press-fit magnets, specify preloaded bearings, conduct modal analysis to avoid resonant frequencies.

Corrosive Environments (Marine, Chemical): Implement IP67/IP68 sealing, epoxy coatings on windings, stainless steel housings, sealed bearings.

Vacuum/Space: Use dry lubricants (MoS₂, PTFE), low-outgassing materials, conduction-only cooling, ceramic or magnetic bearings.

Electric Motor Design with SimScale

SimScale’s cloud-native platform offers comprehensive multiphysics simulation without hardware limitations, running directly in web browsers.

Electromagnetic Design and Simulation

Analyze magnetic fields, calculate torque and eddy current losses, and determine winding inductance and resistance. SimScale’s magnetostatics and time-harmonic magnetics capabilities enable engineers to optimize motor designs before physical prototyping.

SimScale provides step-by-step guides such as the Time-Harmonic Electromagnetics Simulation on a 3-Phase Transformer and the Electromagnetics Simulation on a Magnetic Lifting Machine.

Thermal Management

Conjugate Heat Transfer (CHT) analysis predicts temperature distribution across the motor. Engineers can design effective air or liquid cooling systems, optimize flow paths, and identify hotspots caused by copper losses and core losses before building prototypes.

Structural Analysis

Finite Element Analysis (FEA) using SimScale’s structural mechanics capabilities assesses mechanical stress on shaft-rotor assemblies, performs modal analysis to avoid destructive resonance, and predicts fatigue life. This ensures structural integrity under high speeds, rapid acceleration, and thermal stresses.

Learn advanced techniques in our Advanced Structural Analysis for Electric Motor Shaft and Rotor Design Webinar.

Structural Validation: Rotor Dynamics at 16,000 RPM

“FEA simulations revealed that centrifugal forces caused the rotor discs to ‘dish’ away from the central stator at high speeds. This was a very helpful discovery, since it effectively works as a self-governing mechanism to prevent contact between the rotors and stator. It’s a critical consideration with an air gap of only 0.6mm.”

Maximilian Güttinger

CEO & Co-founder, Emil Motors

Cloud-Based Optimization

SimScale’s cloud infrastructure provides:

• Scalability: Run unlimited parallel parametric studies exploring entire design spaces
• Accessibility: Complex multiphysics models run in any web browser, no specialized hardware required
• Collaboration: Teams share projects globally via URLs, enabling real-time feedback

This approach reduces simulation time from weeks to hours while eliminating expensive workstation requirements.

Ease of Use Advantage

The team delivered an optimized design in just 2 weeks and identified 2 major design changes, enabling them to compete in the American Solar Challenge and Bridgestone World Solar Challenge.

Electric Motor Design: Cost and Timeline Considerations

Understanding investment requirements helps engineers plan effectively.

Budget Ranges by Motor Category

Development Timelines

Standard Development: 16-31 weeks (4-7.5 months) including requirements, design, prototyping, testing, and iterations.

Accelerated with Simulation: 8-16 weeks (2-4 months). Parallel simulation studies reduce iteration cycles by 40-60%, and virtual prototyping eliminates 1-2 physical prototype cycles.

Certified Motors: Add 6-12 months for UL, CE, or MIL-SPEC certification and environmental qualification.

How Simulation Reduces Costs

Traditional Approach: 3-5 physical prototype iterations at $5,000-$30,000 each = $15,000-$150,000 total.

Simulation-Driven: Virtual prototypes reduce physical builds to 1-2 validation units only.

Savings: $10,000-$100,000 per project plus 8-16 weeks accelerated time-to-market.

SimScale Advantage: No $15,000-$40,000 workstation investment, unlimited parallel optimization, and global team collaboration enable faster innovation cycles.

Start Simulating Now

Electric Motor-Driven Systems account for over 40% of global electricity use. Marginal efficiency improvements are critical for sustainability targets and meeting global MEPS regulations.

The ultimate challenge for motor OEMs is consistently delivering high-performance, high-efficiency designs while minimizing Total Cost of Ownership. Cloud-native simulation in SimScale provides the speed and multiphysics insight to replace slow, costly physical prototyping.

SimScale offers cutting-edge solvers integrated into a cloud-native interface, enabling multiple parallel simulations directly in your web browser—no expensive hardware required.

The post Electric Motor Design appeared first on SimScale.

On-Demand Webinar

Want the full picture? Watch the on-demand webinar on democratizing advanced nonlinear simulation with Marc and AI.

Watch On Demand

1. The Simulation Bottleneck Is Breaking Down

If you’ve ever waited days for a simulation expert to re-run your updated design, you know the traditional workflow is broken. Designers run simplified linear analyses in their CAD tools, then hand off to specialists for the real nonlinear studies — and what follows is a slow loop of emails, PDFs, and meetings while you wait for each iteration.

SimScale collapses this. On a collaborative, cloud-native platform, both you and your simulation experts work on the same project. Template simulations designed by specialists can be reused and adapted for new designs, while AI agents provide guidance and catch errors. You get faster iterations without sacrificing the depth that nonlinear analysis demands.

2. The Real World Is Nonlinear — And Marc Is Built for It

If you’re relying solely on linear analysis, you’re working with an approximation. The real world doesn’t behave linearly — materials yield, creep, and undergo stress relaxation. Rubber seals stretch and recover. Thermoplastics soften permanently under repeated loading. Parts come into and out of contact, slide, stick, and separate. And loading sequences matter — the same final load can produce entirely different outcomes depending on the path taken to get there.

Marc, the world’s first commercial nonlinear FEA code (since 1971), was purpose-built for these challenges. With over 30 analysis classes, it offers fully coupled structural, thermal, and electromechanical solutions alongside advanced material models — from hyperelastic rubber to viscoplastic materials with damage, creep, and permanent softening. What makes it robust? Automatic contact detection that tracks evolving conditions as parts deform, automatic remeshing when elements distort severely, and intelligent load stepping that adapts as the physics demands. These are exactly the capabilities you need for complex contact scenarios and large-deformation problems.

3. Complex Simulations, Surprisingly Simple Setup

In a live demo, Richard set up a nonlinear rubber bushing simulation from scratch — hyperelastic materials, self-contact with friction — in just minutes, directly in the browser. Import your CAD, let automatic contact detection handle the assembly, assign a Mooney-Rivlin hyperelastic model, define your loads, and run. Everything executes on the cloud with up to 192 cores on a single license.

He also demonstrated sequential loading: applying a 500 N force first, then locking it in place and applying a 45° rotation — configured through simple load steps in the UI. This kind of multi-step scenario is critical for realistic component assessment, and Marc handled the path-dependent behavior seamlessly.

Need to iterate? Duplicate the simulation, tweak the CAD, and re-run — boundary conditions automatically reassociate to the new geometry. Multiple variants run in parallel without touching your local machine. And the built-in Engineering AI agent even caught a unit error (Pascals instead of megapascals) before the simulation launched.

4. AI Agents Automate Your Entire Simulation Workflow

Imagine giving an AI agent a 78-component gearbox assembly and a single instruction. That’s exactly what the webinar demonstrated. A custom “gearbox assessment” agent autonomously identified every component, assigned materials, defined bolt preloads to internal specifications, applied gear loads and fixtures, and launched the simulation — no manual setup required.

A second example went further: a bike frame design workflow where the agent ingested an RFQ document and a CAD file, set up the required load cases, ran them, compared results against specification limits, and recommended design changes when the initial design fell short. You define the agent once with your organization’s best practices, material libraries, and specs — then it handles the rest, consistently, every time.

5. Physics AI Unlocks the Nonlinear Design Space

Here’s a challenge you may not have considered: the design space for nonlinear problems grows exponentially. Material parameters, contact friction, geometric imperfections, loading sequences — the permutations multiply fast. And because nonlinear analysis is path-dependent, changing a parameter even slightly doesn’t just shift results — it can change the entire system evolution.

This is where Physics AI complements Marc. By training surrogate models on a relatively small number of high-fidelity Marc simulations, you can predict outcomes across thousands of design variations almost instantly. The surrogate models learn the nonlinear response surfaces, letting you explore the full design space rather than guessing at isolated points. And since everything is integrated on SimScale, you can switch between surrogate predictions and full Marc validations at any point — speed when you need it, fidelity when it matters.

What’s Next: Marc on SimScale Roadmap

The session previewed what’s coming: coupled thermomechanical analysis by end of Q1 2026, temperature-dependent materials in Q2, dynamics analysis in Q3, and adaptive mesh refinement later in the year.

Watch Now

Don’t miss out on the full experience and deeper insights into how SimScale’s latest features can transform your engineering workflow. Watch the complete webinar on-demand to see these tools in action and understand how they can be applied to your specific challenges. Click here to access the webinar recording and start accelerating your design process today!

The post Webinar Highlights – Democratizing Advanced Nonlinear Simulation with Marc and AI appeared first on SimScale.

Until now, our progress was shared through occasional blog posts, social media updates, and other external communications. However, we felt we were missing a dedicated space to showcase and provide real-time updates on our sustainability journey.

Today, we are thrilled to announce that SimScale has launched its very own Sustainability Site!

What can you expect to find?

Our new Sustainability Site clearly defines the values underpinning our strategy and the specific tactics we execute quarter by quarter. It serves as the official source for up-to-date information regarding our progress. Key features include:

• Corporate Carbon Footprint: Transparency is vital. We detail our impact across every category year-over-year, allowing anyone to track the progress we’ve made since we first began measuring our footprint.
• Customer Success Stories: We invite you to learn about clients who have accelerated their own sustainability goals by integrating SimScale into their development and production workflows.
• Annual Impact Metrics: Get a high-level overview of our progress over the last twelve months, including ecological activities (such as trees planted), safety reports, employee engagement scores, and ESG training hours.

Join the Conversation

This site is a living project, and we want it to reflect what matters most to our community. Is there other information you would like to see on our sustainability site? Get in touch with us and let us know your thoughts!

Stay tuned for more insights into SimScale and see what the team has been up to on our @lifeatsimscale Instagram feed. Want to start your own SimScale story? Make sure to keep an eye on our careers page for possible openings!

The post Shaping a Sustainable Future: Introducing SimScale’s New Sustainability Site appeared first on SimScale.

It used to be the case that proposals were gathered (in a leisurely manner) and then a selection made according to the customer’s preferences for quality or cost. But now there is another consideration – bid speed. 

Accelerating RFQ/RFP responses: Why the rush?

In many fast-moving and competitive industries – let’s take the automotive industry as an example – the timeframe for an RFQ has shrunk significantly. This is driven by the need to get to market sooner because of a fast moving technology backdrop. In the case of the car industry, this is driven by electrification and battery technology.

Traditional RFQ processes often stretch over several days or even weeks, involving multiple handoffs between engineering, simulation and commercial teams. Each step, from interpreting requirements to running simulations and coordinating design updates, is typically done manually and across disconnected tools.

To get your competitive and de-risked bid over the line first, all that up-front engineering work still needs to happen. Just now it has to be much, much faster.

Beat your competition with agentic AI

Imagine if your business could respond to RFQs in a matter of hours, rather than days or weeks? It would win you more bids, but what would it take?

Interestingly, while few organizations have fully embraced AI in their engineering workflows, the gap isn’t usually due to technical constraints. More often, it’s the result of legacy systems, limited access to data or internal resistance to change. The reality is that effective automation is already achievable by guiding AI with familiar engineering inputs, like geometry, materials, loads and boundary conditions, and allowing it to manage repetitive tasks such as simulation setup, execution and iterative design updates.

RFQ automation workflow 

At a high level, the workflow follows six steps:

1. Upload RFQ documents and CAD geometry
   
2. Extract requirements using AI
   
3. Automatically prepare and run simulations
   
4. Evaluate results against requirements
   
5. Apply design improvements and re-simulate
   
6. Generate a final report that could be customized for the customer
   

To see how this works in practice, explore the interactive demo below. It walks through the same RFQ automation workflow described above, showing step by step how an RFQ progresses from document and CAD intake to fully validated results – quickly, autonomously and with engineers in the loop. 

Keeping engineers in the loop

Although the RFQ automation workflow operates from start to finish with minimal manual effort, it’s intentionally designed to avoid becoming a black box. One common myth about automation is that it sidelines human judgment. In practice, the most effective systems are those that involve engineers exactly where their expertise has the greatest impact.

This aligns with the growing shift toward human-in-the-loop AI, where intelligent agents take care of repetitive, structured tasks, while engineers retain control. At every stage, engineers can:

• Review extracted requirements
• Track simulation progress
• Assess CAD modifications
• And examine detailed simulation outputs

The workflow remains fully transparent and flexible – it can be paused, adjusted or investigated at any time.

Business impact of RFQ automation

Implementing end-to-end RFQ automation delivers measurable business value that extends well beyond simple productivity improvements. It fundamentally changes how quickly teams can respond to customer requests, how efficiently engineering resources are used and how reliably high-quality proposals are generated.

Key business advantages include:

• Major time savings: RFQ turnaround is cut from days or weeks to just hours, supporting faster decisions and increasing the likelihood of winning new business
• Eliminated engineering bottlenecks: Routine setup and analysis work is handled by the system, allowing engineers to concentrate on strategic design and validation tasks
• Accelerated customer engagement: Shorter response times enable teams to interact earlier and with more confidence during the sales process
  
• Higher-quality proposals: Consistent, simulation-driven insights and optimized designs help produce more accurate and competitive quotes

Conclusion

End-to-end RFQ automation is transforming the way engineering teams handle customer requests. What used to involve multiple tools, time-consuming handoffs and weeks of manual effort can now be executed through a single, integrated workflow, from RFQ intake and requirement extraction to simulation, design refinement and final report generation. By combining AI-driven intelligence with automated analysis and optimization, teams can respond faster, scale effectively and deliver consistently high-quality, engineering-validated proposals – all without losing visibility or control.

If you’d like to discuss how RFQ automation could fit into your own engineering workflows, feel free to get in touch with our team.

The post RFQ Response Automation – Speed is Survival appeared first on SimScale.

This Autumn, we are excited to release a suite of new features focused on automation, solver robustness, and expanded physics capabilities. From the introduction of our new Engineering AI Agent to major upgrades in our FEA and CFD solvers, this update is designed to shorten your simulation lead time and accelerate innovation.

Here is an overview of the key updates from the Autumn 2025 release.

On-Demand Webinar

If the highlights caught your interest, there are many more to see. Watch the on-demand Simulation Expert Series webinar from SimScale on how real-time simulation with AI is driving faster design cycles and superior products by clicking the link below.

Watch On Demand

SimScale AI: Agentic Automation

We are taking a significant leap forward in how users interact with simulation through our combined AI technologies: Engineering AI and Physics AI.

• Workbench Agent (Beta): The first step in our Engineering AI roadmap, the Workbench Agent acts as an intelligent assistant within the platform. It can help validate setups, answer simulation queries using documentation, and even automate workflows by reusing setups from previous projects.
• Physics AI Integration: Engineering AI can now leverage Physics AI to perform rapid design optimizations, allowing you to predict results instantly before validating the final design with a traditional solver.

To experience the power of Engineering AI and Physics AI together, click through the demo below. If you would like to take part in our beta program, contact your support team.

Ray – AI Chat Support:

Available to all users, Ray is our 24/7 AI support assistant. Ray can visually diagnose error messages from screenshots and provide instant troubleshooting steps, ensuring you are never stuck waiting for a resolution.

Ray is designed to be a SimScale expert. It has memorized our entire documentation and is trained on simulation best practices, as well as the most common issues and workflow questions you are likely to encounter.

Ray helps to get the information you need as quickly as possible, however your dedicated human engineer is still here for complex, deep-dive project support.

Computational Fluid Dynamics (CFD)

This quarter brings significant quality-of-life improvements and feature parity to our CFD solvers, particularly for rotating machinery and thermal management.

• Variable Time Steps (Multi-purpose): You can now define variable time steps for transient simulations. This allows users to start with coarser steps to establish flow stability and switch to finer steps for high-accuracy resolution, significantly optimizing runtime without compromising quality.
• Run Continuation for CHT (IBM): Users running Immersed Boundary Method (IBM) Conjugate Heat Transfer simulations can now continue transient runs or steady-state simulations from where they left off. This saves valuable computing resources by eliminating the need to restart long simulations from zero.
• Periodic Boundary Conditions: Efficiently simulate complex, repetitive structures (like heat exchangers) by modeling only a single unit cell or subset, reducing model size and computation time.

Finite Element Analysis (FEA)

Following the integration of the Hexagon Marc solver earlier this year, we have released a massive set of new capabilities to handle complex nonlinear structural problems.

• Load Steps: You can now define multiple load steps within a single simulation run. This is essential for manufacturing processes like pipe bending or rubber seal compression, where constraints and loads change sequentially during the event.
• Automatic Contact Detection and Glued Contact Tolerance: For large assemblies with 10+ parts, SimScale now automatically detects and glues contact pairs. This automation drastically reduces setup time, allowing you to focus only on the specific contacts that require manual definition.
• Contact Forces and Pressure: Get deeper insights into nonlinear simulations involving contact by reporting on forces, stresses, contact gap/state and contact pressure.
• Advanced Material Models:
  • Marlow Hyperelastic Model: Drive hyperelastic material behavior directly from test data without complex parameter fitting.
  • Viscoplastic Materials: Accurately model creep, relaxation, and damage in polymers under cyclic loading.
• Remote Displacement & Force: Apply distributed forces or rigid body motions (including rotations) via a central pilot point using new RBE2/RBE3 connectors.
• Symmetry Constraints: We have introduced a plane-based symmetry condition, allowing you to model only a half or quarter of your geometry to save computational effort while maintaining full-model accuracy.

Electromagnetics

Our electromagnetic analysis capabilities continue to grow, with a focus on high-frequency efficiency and power electronics.

• Litz Wire Modeling: You can now model complex bundles of insulated wire strands as a single equivalent geometry. This preserves key electrical and thermal characteristics while avoiding the computational heavy lifting of modeling every individual strand.
• Time-Periodic Acceleration: For transient magnetic simulations driven by periodic excitations (e.g., PWM or sinusoidal waveforms), this feature accelerates the solver to reach steady-state results in a fraction of the time.

General Platform Enhancements

We have also introduced several non-physics updates to improve the overall user experience and workflow efficiency.

• CAD Section View: Inspect the internal details of complex 3D CAD models by slicing them along a cutting plane directly in the pre-processor.
• Cancel CAD Operations: You now have the ability to stop long-running CAD operations midway, giving you better control over your preparation workflow.
• Automatic Extrusion Meshing: The Standard Mesher now automatically detects the optimal direction for extrusion (longest or shortest dimension), ensuring higher quality meshes for elongated bodies and thin plates.
• Pedestrian Wind Comfort (PWC) Cutting Planes: Post-processing for PWC analysis now supports cutting planes, allowing you to visualize comfort plots hidden by complex geometry like canopies or overhangs.

Watch Now

Don’t miss out on the full experience and deeper insights into how SimScale’s latest features can transform your engineering workflow. Watch the complete webinar on-demand to see these tools in action and understand how they can be applied to your specific challenges. Click here to access the webinar recording and start accelerating your design process today!

The post Webinar Highlights – SimScale Autumn 2025 Product Updates appeared first on SimScale.

But are we experiencing the same in mechanical engineering?

Our State of Engineering AI survey suggests that only 3% of hardware engineering companies are seeing comparable ‘significant’ gains from adopting AI in their workflows. Why such a small number? One of the most commonly cited reasons was the inflexibility of legacy software.

Luckily, there is a whole raft of new AI tools for mechanical engineers emerging, covering the whole product lifecycle. Let’s dive in.

1. Ideation: Beyond the Blank Page

The conceptual design phase sets the foundation for an entire project. Traditionally, this relies on an engineer’s experience and intuition to sketch out a few potential solutions. AI, specifically through generative design, challenges this paradigm.

Generative Design

Generative design tools use algorithms to explore thousands of potential design solutions based on a set of constraints you define. You input the non-negotiable parameters—functional requirements, material properties, manufacturing methods, and performance criteria—and the AI generates a massive number of high-performing options. Your role shifts from being a generator of a few ideas to a curator of many.

Autodesk Fusion 360

Autodesk’s platform uses cloud-based machine learning to automatically generate and rank design solutions. As an example, check out this case study with General Motors, which used generative design to redesign a seat bracket. The AI-generated result was a single, organic-shaped part that was 40% lighter and 20% stronger than the original component, which had previously consisted of eight separate parts.

nTop 

This tool excels in creating highly complex, performance-critical components. For instance, Cobra Aero used nTop to redesign a drone engine cylinder. Instead of traditional cooling fins, the software generated an intricate internal lattice structure. This AI-driven design significantly reduced weight while improving thermal performance—a result that would be nearly impossible to arrive at through traditional design methods.

2. Drafting, Design and Review: The Intelligent Drawing Board

Once a concept is chosen, it must be translated into a detailed CAD model. AI is now being embedded directly into CAD software to serve as an intelligent co-pilot, automating repetitive tasks and streamlining the design process.

AI-Assisted CAD

This category includes AI features that automate common tasks like dimensioning, applying constraints, design reviews and compliance checks. These tools learn from your design habits and company standards to make intelligent suggestions, reducing errors and saving significant time.

Onshape AI Advisor

This tool acts as an intelligent assistant directly within the cloud-native CAD environment. An engineer can ask natural-language questions (e.g., “How do I create a variable-pitch helix?”). The AI provides step-by-step recommendations, troubleshooting help, and best practices drawn from Onshape’s training materials, effectively accelerating the learning curve and day-to-day workflow. This cloud-native approach is also critical for AI-driven simulation, as it allows parametric CAD models to connect directly to analysis tools like SimScale, enabling the rapid, automated iteration that AI requires.

Shapr3D

Shapr3D bypasses the hours typically spent in dedicated rendering software with its embedded AI Visualization. By simply positioning a model and typing a text prompt (e.g., “modern kitchen counter”), engineers can generate context-aware renders in seconds. The tool uses your specific CAD geometry as a guide, allowing for rapid concept validation and “mood boarding.” Features like Variation Intensity let you control the AI’s creative freedom, enabling you to present polished, stakeholder-ready visuals instantly without leaving the design environment.

Intelligent Design Review (ReviewOps)

CoLab Software

While tools like Onshape and Shapr3D streamline CAD creation, CoLab addresses the bottleneck of reviewing it. Traditionally, critical feedback gets lost in “data graveyards” of static screenshots, email threads, and PowerPoint decks. CoLab’s AutoReview utilizes AI to automate the administrative heavy lifting of this process, scanning models against your specific design standards before a human ever looks at them.

This acts as an intelligent “pre-flight check,” automatically flagging routine errors like missing hole callouts or GD&T non-compliance. By filtering out this administrative noise, the AI allows senior engineers to focus their expertise on complex problem-solving rather than checklist verification. This effectively closes the loop between design intent and manufacturing reality, ensuring faster, higher-quality feedback cycles.

3. Simulation and Optimization: No More Waiting

For many engineering teams, this is the most painful part of the product development lifecycle. Traditional simulation (CAE) is a well-known bottleneck, but it’s actually two problems. The first is simulation cycle time: the hours or days you wait for a complex computation to run. The second, and often bigger, problem is the simulation lead time: the days or weeks spent by specialists manually setting up physics, preparing geometry, and re-meshing every new design variant. This ‘test-and-wait’ workflow means engineers can only analyze a handful of designs. Engineering AI is the solution to this entire bottleneck, tackling both problems at once to achieve one goal: no more waiting.

Engineering AI (Solving the Lead Time Bottleneck)

This category of AI acts as an intelligent co-pilot to automate the complex, multi-step setup process. Unlike a traditional macro or script which follows a rigid list of commands, Engineering AI uses Large Language Models (LLMs) to reason through the physics of your model.

SimScale Engineering AI

SimScale has introduced an agentic AI assistant that resides directly in the simulation platform. It transforms how engineers interact with simulation through three core capabilities:

1. Democratization for Novices: Traditionally, simulation required years of specialized training. Engineering AI lowers this barrier by guiding novice users step-by-step. It can diagnose missing inputs, suggest appropriate settings based on the geometry, and flag potential errors before a simulation is run. This turns the platform into a mentor, helping junior engineers get to valid results faster.
2. Promoting Best Practices: For larger organizations, consistency is key. Engineering AI can be configured to enforce company-specific “Gold Standard” settings. The agent ensures that every simulation—whether run by an expert in Germany or a novice in the US—adheres to the same quality standards and methodologies, reducing the risk of human error.
3. Reasoning and Adapting: Unlike rigid scripts, the agent uses reasoning to navigate deviations. If a geometry changes slightly or a parameter is missing, the agent evaluates the context to adapt its approach rather than failing.

Physics AI (Solving the Computation Bottleneck)

This is the surrogate model. It’s a lightweight, data-driven approximation of a high-fidelity simulation. An AI model learns the complex, non-linear relationships between inputs (geometry, boundary conditions) and outputs (performance, stress, temperature). Once trained, this ‘Physics AI’ model provides near-instant predictions, solving the computation time bottleneck.

SimScale’s Integrated AI Platform

SimScale has integrated this technology directly into its workflow. This approach was highlighted in a real-world case study by Convion, a part of HD Hyundai, who needed to optimize a complex hydrogen ejector pump.

The Challenge: Armin Narimanzadeh, Senior Thermofluids Expert at Convion, faced a multi-objective optimization problem. The design was complex, and using traditional CFD-driven optimization to find the best-performing design would have taken months.

The AI Solution: Using SimScale, Armin’s team first generated a training dataset by running hundreds of simulations in parallel, mapping out the design space. This data was used to train a reusable Physics AI model within the SimScale platform. The results were transformative. The team now has an AI model that can generate a new, optimized design in under an hour.

This “months to hours” transformation is a perfect example of AI’s power. It was made possible by a fully cloud-native toolchain: a parametric model built in Onshape was connected via an API to SimScale, allowing the AI to automatically test hundreds of variants to find the optimal design. The Engineering AI component automates this workflow, while the Physics AI component provides the instant predictions.

Learn more about the full process by watching the AI Engineering Bootcamp webinar (Session 1) on-demand.

4. Deployment: From Smart Manufacturing to Live Maintenance

AI’s role doesn’t stop when the design is finalized. It extends into the manufacturing process and the operational life of the product (deployment). Increasingly, smart manufacturing systems 

Smart Manufacturing

AI is streamlining the path from a 3D digital model to a physical part. CAM software is using AI to automate the complex and error-prone task of CNC programming, which is a major bottleneck due to a shortage of skilled machinists.

Siemens NX CAM

NX CAM uses AI for “Feature-Based Machining”. It automatically analyzes a 3D model and recognizes geometric features like holes, pockets, and slots. It then suggests the most suitable machining operations and sequences, even learning from a programmer’s past choices to improve future suggestions.

Real-time Digital Twins

Once a product is deployed, AI can help you operate it more effectively by building a real-time digital twin. A Physics AI model fed with boundary conditions from a live system can provide crucial insight into function and health, including ‘virtual sensing’ of metrics which might be impossible to directly measure. Machine learning can also be used to predict when it will fail before it happens, based on historical operational data.

Physical AI

Connect live sensor data streams from your operational assets or system-level models directly to Physics AI surrogates via SimScale’s open API. This creates a closed loop where real-world conditions continuously inform and update your digital twin for maximum accuracy.

By combining live data and hardware-in-the-loop (HiL) setups with AI models, you move from reactive maintenance to predictive operations. This allows you to forecast performance degradation and potential failures before they occur, preventing costly downtime and improving asset reliability.

5. The Next Frontier: Multi-Agent Workflows

While individual AI tools are powerful, the future of engineering automation lies in multi-agent systems. Imagine a “digital engineering team” where specialized AI agents collaborate to execute complex, cross-functional tasks without constant human hand-holding.

In a multi-agent workflow, different agents act as specialists—one might be an expert in reading requirements, another in CAD generation, and another in physics simulation. They communicate with each other to complete an objective that spans multiple software platforms.

Synera

Synera, a leading platform for engineering process automation, is pioneering the use of connected AI agents. In the recent AI Engineering Bootcamp (Session 3), Ram Seetharaman (Head of AI at Synera) demonstrated a live multi-agent workflow that orchestrates the entire design-simulate-iterate loop.

In this example, a “Manager Agent” in Synera interprets requirements and delegates tasks. It triggers a “Geometry Agent” to modify a design logic, which then hands off the new geometry to a “Simulation Agent” (powered by SimScale) to validate performance. The results are fed back to the Manager, which decides whether to iterate further or finalize the design.

By chaining these agents together, you eliminate the coordination bottlenecks that often stall projects for days or weeks, allowing for continuous, “always-on” engineering operations.

The Engineer’s Future: AI as an Amplifier

Across all these stages, the theme is the same: AI is an amplifier for engineering expertise. It tackles the repetitive, time-consuming, and data-heavy tasks that slow down innovation. This allows you, the engineer, to focus on what you do best: problem-solving, creativity, and making the critical decisions that lead to breakthrough products. The companies and engineers who embrace these tools today will be the ones shaping the future of the industry.

The post AI Tools for Mechanical Engineers: Transforming Your Workflow appeared first on SimScale.

“The moment you push performance limits, heat becomes the enemy that never sleeps.”

Alexander Fischer

Co-founder & Product Manager, SimScale

Electric vehicles depend on compact thermal architectures that keep batteries and power electronics within a narrow operating windows. AI accelerators concentrate extraordinary wattage into small footprints. Industrial automation, renewable energy hardware and medical technology all follow the same pattern.

They raise performance expectations while shrinking available space. This creates a new reality in which cold plate design becomes a strategic engineering function rather than a late stage add on. Teams that recognize this shift early gain more performance, more reliability and more control over how their products evolve.

Cold Plate Simulation in Action

In the webinar below, we address a common engineering challenge: redesigning a battery cooling plate to maintain thermal efficiency while adapting to a new, low-power pump specification.

The Practical Challenges Facing Design Teams

Engineering teams face real constraints. They must balance:

• manufacturability,
• pressure drop,
• integration,
• weight targets,
• and routing!

You often work within tight envelopes while trying to handle rising heat flux. Parametric CAD can slow the process because feature trees resist change and complex channels break easily when edited. Conservative geometry becomes the default. This is risky as thermal loads continue to rise across industries. Cold plate cooling demands broader concept exploration, faster iteration and clearer structure throughout the development process.

A High Level View of the Cold Plate Design Workflow Step by Step

A typical cold plate project moves through several major steps from concept to validated geometry.

• It begins with requirement gathering where engineers define heat flux levels, target temperatures, available space, allowable pressure drop, material constraints and manufacturing options.
• Next comes the architectural exploration where macro level decisions such as cooling method, channel layout, inlet and outlet placement and flow balance strategies are evaluated.
• Concept modeling follows with early geometry that tests feasibility and identifies potential performance issues.
• Detailed design development then refines internal channels, surface area enhancements, flow paths and structural supports.
• In parallel, system level integration ensures correct fit and interaction with electronics, enclosures and the larger cooling loop.
• The final stages focus on simulation driven optimization, design for manufacturability and preparation for prototyping.

High performance applications cycle through these steps rapidly as iteration speed becomes a core advantage.

How Implicit Modeling Transforms the Design Phase

Implicit modeling fits directly into this workflow and accelerates it significantly. Traditional parametric CAD relies on sketches, constraints and feature trees. Implicit modeling uses continuous mathematical fields to define form.

Complex shapes become easy to create and sturdy during modification. Families of designs can be generated quickly without model failures. Smooth blends are inherent. Microchannels, graded thicknesses, TPMS surfaces or lattice supported walls appear without manual surfacing.

This matters because cold plate cooling often benefits from organic or highly detailed internal geometry that explicit modeling tools struggle to express.

Why Advanced Cooling Geometry Matters Now

This shift aligns perfectly with the pressure placed on modern hardware. EV power electronics keep increasing in output while packaging shrinks. AI hardware demands targeted thermal strategies that match component level heat flux. Data centers monitor every watt because cooling efficiency now affects operating cost directly. Aerospace, hydrogen systems and compact industrial machinery all follow similar trends. They require high performance cooling solutions that combine low weight, high efficiency and manufacturable complexity.

Cold plate design sits at this intersection because it enables direct heat removal and supports structurally complex yet lightweight geometries.

The Impact of Simulation and AI Assisted Optimization

When advanced modeling is paired with CAE simulation or AI driven physics prediction, the later stages of the workflow become dramatically more effective. Engineers can apply cold plate topology optimization to reshape channels for uniform thermal behavior. Microchannel networks can align with localized heat flux. TPMS or lattice structures can increase surface area while keeping weight low. Iteration becomes flexible and exploration becomes normal rather than exceptional. Cold plates evolve into highly tuned components tailored to the exact demands of each device.

Key Insights

1. Microchannel cold plates deliver high surface area for extreme heat flux handling
2. TPMS and lattice structures enable lightweight internal geometries with strong manufacturability profiles
3. Implicit modeling and topology optimization accelerates every design stage and supports shapes that parametric tools struggle to represent
4. Simulation driven workflows improve accuracy and bridge the gap between concept and validated performance
5. Cold plate design has become a strategic differentiator for any product facing rising thermal loads

Cold plates are no longer secondary components. They enable the future of mobility, computing and energy systems and they reward engineering teams that prioritize them early in development.

The post Cold Plate Cooling Design appeared first on SimScale.

For decades, the language of design has been based on surfaces, sketches, and constraints.

Engineers have grown used to constructing geometry one step at a time – extruding, sweeping, or filleting to build models layer by layer.

But what happens when geometry is no longer described by a sequence of operations, but instead by mathematical fields and equations?

That’s the shift implicit geometry modeling brings. It’s not a new CAD feature or another incremental tool. It’s a fundamentally different way of thinking about how objects are created, changed, and optimized.

Even better, implicit modeling unlocks powerful opportunities to integrate with AI algorithms, enabling advanced shape and topology optimization.

The changing landscape of design

Every product engineer knows the trade-off between creativity and control. Traditional parametric CAD excels at precision, repeatability, and manufacturability—but struggles with complexity and adaptability. The moment a design needs to evolve beyond its original constraints, the model often breaks. Surfaces fail to regenerate. Feature trees become tangled. Performance and hardware requirements add to the challenge. The geometry, instead of serving creativity, starts limiting it.

At the same time, AI simulation-driven design and optimization are moving to the center of product development. Engineers want to explore hundreds of design iterations, automatically test performance, and converge on the best possible shape.

Traditional CAD, built around static geometry, simply can’t keep up. Implicit modeling offers an answer.

What is implicit modeling?

Implicit geometry modeling represents 3D shapes using mathematical functions rather than explicit surface definitions.

Instead of describing a solid by its boundaries (as in B-Rep or mesh-based systems), an implicit model defines a region of space where a function equals zero—the so-called implicit surface. This allows for smooth, continuous transitions, blending, and deformation at any scale without worrying about topology or feature dependencies.

In practice, this means you can modify or combine complex geometries – like lattices, organic forms, or porous structures – using simple operations. Shapes can be added, subtracted, or morphed together using equations instead of manual CAD features. The result is a workflow that is more robust, more flexible, and dramatically faster when exploring non-traditional geometries.

Implicit vs. traditional modeling

To understand the impact, consider a typical CAD-based workflow.

1. You start with sketches
2. define constraints
3. extrude features
4. and trim surfaces.

Every change requires the system to recalculate dependencies. It’s precise, but fragile.

Now imagine instead defining the same geometry as a mathematical field. You can modify it globally – smooth transitions, blend regions, or adjust material density – without breaking any relationships.

This difference has huge implications for design automation and optimization. Implicit models can directly interface with algorithms that search, test, and evolve geometry automatically. They’re also inherently compatible with lattice generation, topology optimization, and generative design tools. Instead of trying to force simulation-ready meshes out of rigid CAD structures, implicit models create analysis-ready geometries by default.

Why now?

Several trends are converging to make implicit modeling more relevant than ever.

First, manufacturing is changing. Additive processes – such as metal 3D printing or high-resolution polymer fabrication—allow the production of complex, non-linear geometries that traditional CAD was never built to handle.

Second, computational power has caught up. With cloud-based platforms and GPU acceleration, implicit models can be calculated, visualized, and simulated in real time.

And third, simulation-driven design and AI-based optimization are entering everyday workflows. Engineers no longer design once and simulate later; they design through simulation. Implicit geometry provides the missing foundation for this level of integration.

Real engineering impact

Implicit modeling isn’t just about generating futuristic shapes—it’s about solving real engineering challenges. Lightweighting, for instance, becomes more than removing material; it becomes a question of continuously varying density to match structural or thermal demands. Fluid flow optimization can be achieved by smoothly adjusting surfaces for better aerodynamics or cooling. Complex lattices can be embedded into structural components without manual feature management.

In fields like aerospace, medical devices, or consumer products, this approach means faster iteration, fewer redesigns, and products that are both lighter and stronger. The design space expands, while the time-to-simulation and time-to-market shrink.

The power of integration: Implicit + Simulation + AI

The real magic happens when implicit modeling connects directly with CAE simulation or AI-driven physics prediction.

Optimizing within the implicit space opens up massive opportunities. The feedback loop tightens. Designers no longer need to spend cumbersome work to rebuild and simplify models for each test or optimization cycle – changes are immediately updated in the field representation.

Powerful simulation technologies using meshless or quasi-meshless Cartesian techniques allow to directly evaluate the design without manual user input.

This synergy unlocks new frontiers for generative design, topology optimization, and AI-assisted shape exploration. Imagine defining not just a component, but an entire system, where materials, structures, and flows are optimized together, automatically, based on real physics.

The essential takeaways

Here’s what makes implicit modeling a quiet revolution in design engineering:

• Continuous geometry control – Modify shapes smoothly without constraint rebuilds or topology breaks
• Seamless integration with simulation – Connects directly with CAE and generative optimization workflows
• Ready for AI and automation – Enables algorithmic exploration and machine learning in design space
• Scalable complexity – Handle intricate lattices and organic structures efficiently
• Accelerated innovation – Iterate faster with fewer modeling bottlenecks and simulation-ready output

Looking ahead

Implicit modeling challenges a long-held belief in engineering: that geometry must be built piece by piece. As more tools adopt implicit representations, designers and engineers will find themselves working less with constraints and more with possibilities. Instead of fighting the model, they’ll collaborate with it—shaping, simulating, and refining in one continuous loop.

For design engineers, this is not just an efficiency gain; it’s a creative shift. It’s the ability to think in systems, not sketches. To design performance into geometry, rather than fitting geometry to performance. And as implicit modeling merges with AI-driven design, we’re seeing the emergence of a new era of computational creativity.

Ready to see how implicit modeling connects with advanced simulation? Explore the partnership here.

The post Implicit Modeling appeared first on SimScale.

Eliminating Bottlenecks

The session brought together three distinct perspectives on how to operationalize AI in production environments: Ram Seetharaman (Head of AI, Synera) on Agentic AI, Matthias Bauer (Director of Software Development, Autodesk / Founder, NAVASTO) on Physics AI, and David Heiny (CEO, SimScale) on the cloud-native infrastructure that binds them together.
The consensus? The industry is moving past the “chatbot” phase. We are entering an era where AI Agents orchestrate complex tools to automate busy work, and Physics AI provides instant feedback loops, allowing engineers to traverse design spaces at unprecedented speed.

Key Takeaways:

1. Agentic AI is the “Digital Engineer,” Physics AI is the “Calculator”

The session clarified the distinction between the two critical types of AI. Agentic AI (using LLMs) acts like a digital employee—reasoning, planning, and orchestrating tools to handle complex processes like RFQ responses. Physics AI (using GNNs) acts as an ultra-fast solver, providing instant performance predictions to accelerate the design iterations that the agents (or humans) generate.

2. Integration is the Multiplier (The “Electric Motor” Analogy)

Matthias argued that simply swapping a solver for an AI model isn’t enough. He compared it to the industrial revolution: replacing a steam engine with an electric motor didn’t yield efficiency gains until factories were redesigned around the new power source. Similarly, AI only delivers ROI when deep-integrated into the tools engineers already use (like CAD), rather than sitting in a silo.

3. Trust Comes from Traceability, Not Blind Faith

A major barrier to AI adoption is the “black box” problem. The panel emphasized that trust is built through auditability. For Agentic AI, this means viewing the “chain of thought”—seeing exactly which tools the agent used and why. For Physics AI, it means statistical validation and “traffic light” confidence scores that tell an engineer when a prediction is reliable and when to fall back to traditional simulation.

4. The “Junior Engineer” Model

AThe most practical way to deploy AI today is to treat it as a “junior engineer.” It can autonomously handle tedious tasks (like meshing, setup, or initial design sweeps) and present 80% complete work for expert review. This keeps humans in the loop for critical engineering judgments while removing the bottleneck of manual execution.

Watch the full webinar recording below. And if this seems interesting, be sure to check out the rest of the series!

The post Webinar Highlights: AI-Native Engineering Workflows appeared first on SimScale.