Accelerating RFQ/RFP responses: Why the rush?

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 a number of simulations. 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.

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.

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.

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AI in engineering is only just starting to break out into the mainstream. We recently surveyed 300 engineering leaders and we found that only 7% had mature AI programs in place, with 42% actively working on pilots, but over half having still not yet started anything serious.

But while we saw widespread acknowledgement that AI in engineering has huge potential, only 3% of companies adopting it are already reaping the significant rewards that are possible. 

Put simply, there is a huge opportunity for engineering organizations to build a competitive advantage by adopting this technology in the right way.

This article cuts through the noise to showcase seven ways that we see AI delivering value to our customers. These are not futuristic concepts; they are tangible solutions that directly address the most time-consuming, error-prone, and knowledge-intensive aspects of engineering.

Simulation Democratization: AI for Every Engineer

Traditionally, physics simulation has been a specialized field, confined to a few experts with access to expensive software and hardware that’s complicated to learn and use. This creates a dual bottleneck: projects stall waiting for expert review, and deep engineering knowledge is difficult to scale across the team.

The AI Solution: The goal of simulation democratization is to put these powerful tools safely into the hands of every engineer. Cloud-native platforms already provide much easier browser-based access, but the real key is Agentic AI acting as a force multiplier for expertise. It captures the proven methods of senior engineers and embeds them into reusable templates and guided workflows. This gives every engineer an AI-powered co-pilot that walks them through complex processes step-by-step, interacting using natural language. Opening up access to simulation in this way ensures consistency and reduces errors, while scaling knowledge across the entire organization.

RFQ and RFP Automation: Winning More Bids

Responding to a Request for Quote (RFQ) or Request for Proposal (RFP) is a high-pressure, make-or-break moment. Engineering teams must quickly assess feasibility, optimize designs, and produce reliable cost estimates under tight deadlines. This often leads to rushed proposals, reduced win rates, and shrinking profit margins.

The AI Solution: AI-driven automation transforms the RFQ process. An Agentic AI can read an RFQ, instantly generate relevant design concepts, and automatically validate them using physics simulation. This allows teams to explore multiple options, identify cost and feasibility risks early, and package proposal-ready reports in hours instead of days.

Accelerating R&D and Product Innovation

Traditional R&D is slow, expensive, and risky. Heavy reliance on costly physical prototyping and limited resources means teams can only explore a handful of design options, leaving breakthrough ideas undiscovered and inflating budgets.

The AI Solution: AI-enhanced simulation tackles R&D challenges on two fronts: cost and speed. It enables virtual prototyping, where thousands of digital experiments replace physical models to cut material waste and lab expenses. Simultaneously, it powers massive design space exploration, where generative workflows create and test thousands of concepts in parallel. This combined approach allows teams to compress development timelines from months to days while making smarter, more cost-effective design choices.

Agentic Workflow Automation: Connecting the Dots

Engineering processes often involve a maze of manual handoffs between different tools and teams for tasks like geometry prep, simulation, and reporting. Each step introduces potential delays and human error, wasting valuable engineering time that could be spent on innovation.

The AI Solution: Agentic workflow automation uses AI to connect these disparate steps into a single, seamless process. AI agents can coordinate everything from CAD input and multi-physics analysis to optimization and reporting, all without manual intervention. By integrating with existing tools like CAD and PLM systems, these automated workflows plug directly into established engineering processes. Check out our recent webinar: The Rise of AI Agents in Engineering – What Can We Expect?.

Real-Time Digital Twins: Bringing Designs to Life

Digital twins are powerful, but traditional versions are difficult to implement, often requiring heavy modeling effort and costly, manual data integration. As a result, the insights they provide often lag behind reality.

The AI Solution: AI is making real-time digital twins a practical reality. The key is using lightweight Physics AI surrogates – AI models trained on high-fidelity simulation data that can run in real-time. By connecting these surrogates to live sensor data from products in the field, engineers can get instant predictive insights into performance, spot potential failures before they happen, and feed real-world data back into the R&D process.

AI in engineering is here – it just needs to get from the lab into the loop

The engineering industry’s gap between AI ambition and execution is a clear signal that a focused approach is needed. The path to closing this gap is not about “doing AI” in the abstract, but about deploying it with purpose. It’s likely that your organization is already pursuing initiatives like those explored in this blog, AI simply redefines what is possible to achieve with them:

The use cases for AI in engineering detailed in this blog—from further democratizing simulation to automating commercial responses—are tangible, real-world applications that directly solve the most significant productivity bottlenecks in the modern engineering simulation process. They are the practical bridge that connects the ambition for AI to its successful execution.

For engineering leaders, the call to action is to shift from tactical experimentation to strategic transformation. This means investing in the open, cloud-native platforms capable of supporting this new generation of embedded, agentic, and collaborative AI. The ultimate goal is not to replace the invaluable expertise of engineers, but to fundamentally enhance it. By creating “machine-in-the-loop” workflows, we can supercharge their creativity, multiply their impact, and free them to focus on what they do best: solving the world’s most complex challenges and engineering the irreplaceable.

Ready to see how AI can transform your engineering workflows?

Get in touch with one of our specialists to learn how you can start applying these use cases today.

The post Real World Engineering Applications of Artificial Intelligence appeared first on SimScale.

Our recent webinar, “The Rise of AI Agents in Engineering,” featuring SimScale’s CEO David Heiny and Application Engineering Manager Dr. Steve Lainé, explored this exciting frontier. Here are five key takeaways from the discussion.

On-Demand Webinar

If the highlights caught your interest, there are many more to see. Watch the on-demand Engineering Leaders 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

1. Agentic AI is a Leap Beyond Traditional Automation

For decades, engineers have relied on rigid scripts and macros for automation. While useful, these tools are often brittle, difficult to maintain, and only viable for highly repetitive workflows where the upfront cost is justified.

Agentic AI is different.

Instead of following a fixed script, an AI agent can:

• Interpret Intent: An engineer can state a high-level goal, and the agent can interpret it to determine the necessary actions.
• Reason and Adapt: The agent uses reasoning to navigate deviations from a standard process, handling unexpected variables that would break a traditional script.
• Leverage Context: It learns from past simulations and organizational best practices to make intelligent decisions, such as applying the correct materials and boundary conditions without explicit, step-by-step instructions.

This flexible, intelligent approach makes automation more powerful and applicable to a wider range of engineering challenges.

2. AI Agents Eliminate Manual, Repetitive Work

A significant portion of an engineer’s time is spent on low-level tasks rather than creative problem-solving and innovation. AI agents are designed to take over this manual work, freeing up engineering teams to focus on high-value activities.

In a live demo, we showed how an AI agent could set up three different simulations in just minutes; a manifold stress analysis, an inverter NVH analysis, and a valve CV assessment. The agent autonomously:

• Created the required analysis type in the platform.
• Assigned materials based on past projects and internal data.
• Applied relevant forces, pressures, and other boundary conditions.
• Launched the simulation to run in the cloud.

By automating these manual setup processes, engineers can get critical performance feedback in minutes or hours instead of weeks, directly addressing the bottleneck of simulation lead time.

3. AI Agents Can Unlock Rapid Design Exploration

The webinar highlighted how SimScale’s unique combination of predictive Physics AI and agentic Engineering AI can work together to dramatically speed up innovation:

• Engineering AI (Agentic AI): This system automates the manual work of setting up and managing simulations.
• Physics AI: This system uses deep learning to accelerate the computational work, predicting simulation outcomes in seconds instead of hours.

When combined, these two systems create a powerful framework for design space exploration. An Engineering AI agent can autonomously generate and test hundreds of design variations, with each one being evaluated almost instantly by a Physics AI model. An example showed this in action, where a centrifugal pump was optimized by evaluating 400 different designs in approximately five minutes. A task that would take hours or even days using traditional solvers and programmatic automation.

4. Trust is Built Through Transparency, Not Black Boxes

A primary concern with AI in engineering is whether its output can be trusted. SimScale’s approach addresses this by making the AI’s process fully inspectable, not a “black box”.

Engineers can review, and even discuss, every step the agent takes:

• every material it assigns,
• every boundary condition it creates,
• and every setting it chooses.

This transparency allows for complete oversight and can agents can operate in a fully automatic or human-supervised manner as desired. Furthermore, teams can implement “guardrails” and provide instructions based on their specific best practices, ensuring the agent operates within established organizational standards for quality and accuracy.

5. The Future is Collaborative, Agent-to-Agent Workflows

In this webinar we showcasing the ‘art of the possible’ in terms of interaction between a human engineer and a single AI agent, the natural first step in embracing this technology. Agentic AI in engineering also opens up a rich set of possibilities for even greater transformation: a collaborative ecosystem where specialized AI agents interact with each other.

Imagine a workflow where:

• A CAD agent generates a new design based on system-level requirements.
• It automatically passes the design to a simulation agent (like SimScale’s) for performance validation.
• The results are then sent to a DFM (Design for Manufacturing) agent to check for manufacturability.

This seamless agent-to-agent communication, managed by an orchestration platform, will further break down silos and accelerate the entire product development lifecycle, allowing engineers to operate at a higher, more strategic level.

Watch Now

Experience the full potential of AI in engineering by watching our on-demand webinar. Delve into detailed demonstrations and discussions to understand how you can leverage SimScale’s AI capabilities in your projects. Watch the full webinar here.

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

However, unlocking the full potential of AI in engineering isn’t just about sophisticated algorithms; it’s fundamentally about data and infrastructure. Many organizations struggle with disparate data volumes, inconsistent folder structures, and scattered spreadsheets, creating a chaotic environment that hinders AI adoption. To truly leverage the transformative power of AI simulation, a robust, centralized data foundation is not just beneficial—it’s essential.

At SimScale, we address this challenge head-on. Our cloud-native platform provides the necessary infrastructure to turn your past engineering data into a strategic asset for future decisions.

In a recent webinar, our experts explored the critical link between data, AI, and engineering, demonstrating how SimScale empowers you to build a successful AI adoption plan – have a look at they 5 key highlights below.

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

1. The data foundation for Physics AI

The need for speed in design validation is more critical than ever, but traditional simulation processes often create bottlenecks. SimScale’s Physics AI dramatically reduces simulation time from hours to seconds, enabling instant physics predictions. This allows engineers to perform multiple iterations quickly, fostering broader design exploration and faster convergence on optimal solutions.

However, the power of Physics AI is unlocked by high-quality engineering data management. This is where SimScale’s cloud-native architecture becomes a game-changer. By centralizing and structuring your simulation data, our platform ensures it is AI-ready. This means you can retrospectively create Physics AI models from simulations that were not originally intended for this purpose, turning your existing data into a valuable resource for future innovation.

2. From data to decisions: A practical example

To illustrate how SimScale transforms data into actionable insights, we’ve created a standalone video demonstrating the end-to-end process. This video showcases how a complex valve simulation can be simplified and accelerated using our AI-powered tools, providing a clear, real-world example of the benefits discussed in the webinar.

3. Optimizing data utilization for AI integration

A critical aspect covered in the webinar is the importance of engineering data management for effective AI deployment. SimScale’s cloud-native platform facilitates the centralization and organization of simulation data, which is essential for training and refining AI models. By ensuring data quality and relevance, SimScale enables engineers to leverage AI more effectively, leading to more accurate and reliable simulation results.

4. Enhancing simulation accuracy with Agentic AI

SimScale’s introduction of Agentic AI, or Engineering AI, represents a significant leap in intelligent simulation environments. This AI form can perform tasks autonomously, learning from each interaction and continuously improving its understanding of complex engineering problems. Engaging with Agentic AI allows engineers to offload routine simulation tasks and data analysis, ensuring that even the most subtle design optimizations are considered, leading to improved product performance and reliability.

5. Streamlining workflows with Engineering AI

The integration of Engineering AI goes beyond individual simulations, facilitating a more holistic approach to engineering workflows. Unlike Physics AI, which focuses on speeding up specific simulation tasks, Engineering AI acts as an intelligent assistant that aids in managing and automating broader aspects of engineering projects. By handling repetitive tasks and providing smart suggestions, it enables engineers to focus more on critical design decisions and innovation. This AI-driven approach ensures that designs not only meet the technical requirements but are also optimized for performance, cost, and manufacturability.

Conclusion: AI in Engineering represents a paradigm shift

The integration of AI within SimScale represents a paradigm shift in how engineering simulations are performed and utilized. By embracing AI technologies like Physics AI and Engineering AI, engineers can not only accelerate their workflows but also achieve higher precision and efficiency in their designs. This revolution in simulation technology is not just about speed but about enabling smarter, data-driven decision-making that propels innovation forward.

Watch Now

To dive deeper into how SimScale is harnessing the power of AI to transform engineering simulations, we invite you to watch the full on-demand webinar. Explore detailed demonstrations and discussions by our experts and learn how these technologies can be applied to your specific engineering challenges. Watch the full webinar here and see first-hand how AI is reshaping the future of engineering.

The post Webinar Highlights – Turn Past Engineering Data Into Future Engineering Decisions appeared first on SimScale.