Accelerating RFQ/RFP responses: Why the rush?

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.

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.

Eliminating Bottlenecks

The discussion broke down the two primary bottlenecks in engineering: simulation lead time (setup) and simulation cycle time (computation), and explored the AI technology that lets you transform engineering processes by effectively eliminating them.

We heard some great insights from Brian Sather from nTop, explaining the importance of a robust geometry pipeline for effective design exploration. Jon Wilde described SimScale’s approach to tackling the bottlenecks in simulation workflows that unlock the full potential of AI-driven engineering.

Key Takeaways:

1. Solving This Needs a Two-Pronged Solution

Physics AI (using GNNs/PINNs) learns physics to deliver instant predictions, crushing the computation bottleneck. Engineering AI (using LLMs) understands user intent to automate and orchestrate entire multi-step processes, crushing the setup and lead time bottleneck.

2. To Scale AI, You Must Solve Data Generation

One of the most significant challenges in scaling AI is assimilating or generating training data. Here, the robustness and speed of geometry generation is key, and traditional CAD models can struggle. We looked at how “computational design” tools can algorithmically generate thousands of valid design variants, creating the synthetic data needed to train a reliable Physics AI model.

3. A Connected Toolchain Is Critical

Eliminating process bottlenecks is only possible with a seamlessly connected toolchain with limited sprawl. The session demonstrated how to build tight, AI-driven optimization loops involving nTop’s implicit models that can be read directly by SimScale, eliminating manual prep work and ensuring a robust transfer from geometry to simulation.

4. AI Agents Are the New “UI” for Democratization

A live demo of SimScale’s Engineering AI agent showed how non-experts can now drive complex simulation much faster. By using natural language (e.g. “optimize this heat sink for me”), a user can trigger an agent to orchestrate CAD, simulation, and optimization in the background. This moves simulation from a specialist-only tool to a capability accessible to the entire organization.

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

The post Webinar Highlights: Scaling AI-Powered Simulation Across Teams appeared first on SimScale.

An AI Masterclass – How to Fit Months into Hours

The highlight of the session was a real-world case study from Armin Narimanzadeh, Senior Thermofluids Expert at Convon (part of HD Hyundai). Armin shared his first-hand experience of using SimScale’s AI-powered simulation to optimize a hydrogen ejector pump, building a reusable Physics AI model that produces instant performance predictions for new designs.

This transformative approach reduced a design optimization process that previously took months down to under an hour, enabling rapid iteration and data-driven decision-making.

The discussion, featuring insights from Mike LaFleche of PTC and Steve Lainé of SimScale, explored the crucial role of a cloud-native ecosystem in making these workflows possible and how to overcome common blockers like data availability and trust in AI.

Key Takeaways:

1. AI is an Amplifier, Not a Replacement for Expertise

A recurring theme was that AI serves as a powerful tool to amplify your engineering expertise. Armin emphasized that while the AI model delivered incredible speed, his engineering expertise was still crucial to guide the optimization, validate the final results against CFD, and make the final design decisions. The goal is to empower experts, not replace them.

2. The “Months to Hours” Transformation is Real

The most powerful takeaway was the quantifiable impact on the product development cycle. Having invested in the initial model training and data generation, Armin’s team now has a reusable AI model that can generate a new, optimized design for their ejector in under an hour. This is a game-changing acceleration that directly impacts business agility.

3. A Cloud-Native Ecosystem was Key

This level of automation and speed is only possible when the entire toolchain is cloud-native. The seamless, API-driven connection between a parametric model in Onshape and the simulation in SimScale was essential for automatically generating and testing hundreds of design variants to firstly map the design space and then to explore and optimize within.

4. You Can Start Now, Even Without Perfect Data

Armin carefully tested different training data sets to find the dataset ‘sweet spot’ – how much data was needed to build an accurate model. He found that the number of samples needed was not as large as originally expected, allowing him to refine his approach for future projects.

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

The post Webinar Highlights: Kickstarting Engineering AI in Manufacturing appeared first on SimScale.

How to Calculate the Pipe Flow Rate

Our calculator determines the flow rate based on the principle of the continuity equation. It’s a straightforward calculation that multiplies the pipe’s internal area by the speed of the fluid flowing through it.

The Flow Rate Equation

The calculator uses the standard formula for volumetric flow rate:

$$Q = A \times v$$

Where:

• \(Q\) is the Volumetric Flow Rate
• \(A\) is the Cross-sectional Area of the pipe
• \(v\) is the Flow Velocity

The cross-sectional area \(A\) is calculated from the given Pipe Inner Diameter (\(D\)) using the formula for the area of a circle, $$A = \frac{\pi D^2}{4}$$. The calculator automatically converts all your inputs into a consistent set of SI units (meters, seconds) before performing the calculation to ensure an accurate result, which is then converted to your desired output unit.

Input Parameters

• Pipe Inner Diameter (D): This is the internal width of the pipe, which defines the space available for the fluid to flow. Common units like millimeters (mm), centimeters (cm), meters (m), inches (in), and feet (ft) are available.
• Flow Velocity (v): This is the average speed at which the fluid is moving through the pipe. It can be entered in various units, such as meters per second (m/s) or feet per minute (ft/min).

Frequently Asked Questions

The post Pipe Flow Calculator appeared first on SimScale.

How to Calculate the Lift Coefficient

Our calculator determines the lift coefficient based on the fundamental lift equation. Here’s a breakdown of the inputs and the formula used.

The Lift Equation

The calculator uses the standard formula for the lift coefficient, which is derived by rearranging the lift equation:

$$C_L = \frac{L}{\frac{1}{2} \rho v^2 A}$$

Where:

• L is the Lift Force
• ϱ(rho) is the Fluid Density¹
• v is the Flow Velocity
• A is the Reference Area

The calculator automatically converts all your inputs into standard SI units (Newtons, kg/m³, m/s, m²) before performing the calculation to ensure a correct, dimensionless result.

Input Parameters

Fluid Density (ρ): The mass of the fluid per unit volume. The calculator includes presets for common fluids like air and water at standard conditions. You can also select "Other" to input a custom density value in either kg/m³ or slug/ft³.

Lift Force (L): This is the component of the aerodynamic force that is perpendicular to the direction of the oncoming flow. It's the force that "lifts" an object, like an airplane wing.

Flow Velocity (v): The speed of the fluid relative to the object (or the object's speed relative to the fluid).

Reference Area (A): This is a characteristic area of the object, typically the planform area (top-down view) of a wing or hydrofoil. For a simple rectangular wing, it would be the chord length multiplied by the wingspan.

Frequently Asked Questions

The post Lift Coefficient Calculator appeared first on SimScale.

How to Calculate Reynolds Number

Our calculator is designed to be flexible and user-friendly, accommodating various scenarios you might encounter. Here’s a breakdown of its features and the calculations we carry out in order to determine the results.

1. Flow Type (Internal vs. External):

• Internal Flow (e.g. through a pipe or duct): Select this if your fluid is confined within a boundary. For these cases, the characteristic length (L) in the Reynolds number formula is typically the diameter for circular pipes or the hydraulic diameter for non-circular ducts.
• External Flow (e.g. over a flat plate, around a sphere): Choose this when the fluid flows around an object. Here, the characteristic length (L) is a dimension of the object, such as the length of a plate or the diameter of a sphere. The Reynolds number for external flow often dictates where boundary layers transition from laminar to turbulent.

2. Fluid Properties (Kinematic vs. Dynamic Viscosity & Density):

The Reynolds Number can be calculated using either kinematic or dynamic viscosity. Our calculator allows you to choose based on the data you have:

• Kinematic Viscosity (ν): This option uses the formula Re = (v x L) / ν. Kinematic viscosity already accounts for the fluid’s density and is often provided for common fluids. Units are typically m²/s or cSt (centistokes).
• Dynamic Viscosity (μ) & Density (ρ): If you have dynamic viscosity and density separately, select this. The calculator will use the formula Re = (ρ x v z L) / μ. Dynamic viscosity (also known as absolute viscosity) represents a fluid’s resistance to shear flow. Units are typically Pa·s (Pascal-seconds) or cP (centipoise).
  • Need to convert? Remember, kinematic viscosity (ν) can be calculated from dynamic viscosity (μ) and density (ρ) using the relationship: ν = μ / ρ.

3. Input Parameters:

• Fluid Velocity (v): The average speed of the fluid.
• Pipe Diameter (D) / Characteristic Length (L):
  • For Internal, Circular Flow: Enter the pipe’s diameter.
  • For Internal, Rectangular Flow: You’ll input the duct’s width and height. The calculator will automatically calculate the Hydraulic Diameter (D_h) using the formula D_h = (4 x Area) / Perimeter. This equivalent diameter is used as the characteristic length for non-circular ducts.
  • For External Flow: Enter the characteristic length relevant to the geometry of the object (e.g., length of a plate, diameter of a cylinder).
• Kinematic Viscosity (ν) or Dynamic Viscosity (μ) and Density (ρ): Input these values based on your “Fluid Properties” selection.

4. Units:

We’ve included common units for all inputs, and the calculator will handle the conversions to ensure accurate results in SI units internally. Just select the unit you’re working with for each parameter.

Frequently Asked Questions

The post Reynolds Number Calculator appeared first on SimScale.

The question is no longer if you should adopt AI in your engineering workflows, but how to do it effectively and at scale. Across industries, engineering leaders are experimenting with AI pilots, but many are finding it difficult to move from isolated experiments to production-scale adoption. The result is often a collection of stalled initiatives and an uncertain return on investment.

For many engineering teams, the core challenge is the lack of a clear blueprint – what does success look like? Unlike other business functions, engineering has unique data and workflow complexities. Your proprietary design data is complex, multimodal, and not easily digestible by the large, general-purpose AI models that have captured the public imagination. A “one-size-fits-all” strategy simply doesn’t work.

A successful AI strategy in engineering isn’t a case of finding one algorithm or system to do everything. The key is to identify points in your workflow where it can add the most value – in other words so you can tackle the most significant bottlenecks in your product development cycle. This is the path from experimentation to transformation.

Two Bottlenecks, Two AIs

The product development process is a race against time. The goal is to shorten the loop from the moment a design is created to the moment its performance is fully understood. This delay is caused by two fundamental bottlenecks:

1. Simulation Lead Time: This is the manual effort and human waiting time. It includes the handoffs between teams, CAD preparation, meshing, and the entire simulation setup process. It’s the time spent preparing to get an answer.
2. Simulation Cycle Time: This is the raw compute time. It’s the hours—or even days—your high-fidelity solvers need to run to deliver a single, accurate result. It’s the time spent waiting for an answer.

To truly accelerate innovation, you need to attack both bottlenecks simultaneously. SimScale does this with a two-pronged AI strategy, deploying a purpose-built AI to solve each problem:

• Engineering AI: An autonomous agent designed to eliminate Simulation Lead Time by automating the entire workflow from setup to execution.
• Physics AI: A predictive system designed to eliminate Simulation Cycle Time by providing instant performance insights without running a full simulation.

Engineering AI in Action: Your New Autonomous Teammate

Consider the following engineering challenge: designing a cold plate for cooling electronics. The goal is to find the right trade-off between thermal performance (cooling) and pressure drop (efficiency).

Usually, an engineer would manually or programmatically set up a simulation for each design iteration—a repetitive and time-consuming process. Engineering AI transforms this. It acts as an autonomous agent that can perceive the simulation context (the geometry, the physics) and execute the entire workflow based on simple instructions. It becomes a new kind of teammate, handling the mundane tasks so your experts can focus on analysis and innovation.

The payoff here isn’t just about speed; it’s about liberating your most valuable engineering talent. By automating the setup, you empower your team to focus on the high-value work of interpreting results and driving design decisions.

Physics AI in Action: Real-Time Insight

Even with a fully automated setup, complex physics can take hours to compute. This inherent delay makes rapid, comprehensive design space exploration impossible.

This is where Physics AI comes in. By training on the results of previous high-fidelity simulations, it learns the physics of your design. It can then infer the performance of a new design variant in seconds, without ever running a full solver. This transforms the workflow from a slow, iterative process into an interactive design session.

As noted in our recent webinar with Ian Pegler from NVIDIA, this capability finally makes comprehensive design space exploration feasible for complex problems. It’s the difference between testing a handful of ideas and exploring thousands.

The Blueprint: How Engineering and Physics AI Work Together

The true revolution happens when these two systems work in concert. They are symbiotic, creating a workflow that is both automated and instantaneous. This is a day in the life of the modern, agent-augmented engineering team:

1. An engineer wishes to kick off a project to optimize a design (let’s say it’s a hydraulic manifold). Instead of digging out CAD files and simulation templates, they instead brief a customized Engineering AI agent in SimScale to perform the study, based on some simple instructions.
2. As part of the study, the AI agent chooses to make use of a relevant pre-trained Physics AI model which can immediately run inferences to provide a real-time prediction of each design’s thermal and hydraulic performance. The engineer, supervising the process, can interactively explore dozens of variations in minutes.
3. The human engineer and Engineering AI discuss the foremost design candidates, selecting a promising variant which demonstrates the desired characteristics spanning performance, cost and manufacturability.
4. Then it is back to the Engineering AI agent to autonomously set up and run a full, high-fidelity simulation in the background for final validation, while the engineer is already moving on to the next creative task.

The total time from a design change to a fully validated performance insight drops from days or hours to minutes. This isn’t a minor improvement; it’s a fundamental acceleration of the entire innovation cycle.

With expert knowledge captured in the AI agent, the whole workflow described above is now accessible to less expert, junior engineers. It is this democratization that allows engineering teams to scale and move faster.

Your Starting Point

Look at your own organization and ask: which bottleneck is slowing you down the most? Is it the manual lead time spent on setup and preparation, or the computational cycle time spent waiting for solvers?

Answering that question will reveal your starting point for a scalable AI strategy that delivers tangible results. If you would like to discuss your strategy with one of our experts, get in touch today.

Catch up on the webinar

Learn more about deploying AI in engineering with SimScale and NVIDIA as part of engineering.com’s Digital Transformation Week

Watch On Demand

The post A Day in the Life of Engineering AI appeared first on SimScale.