Product Name: SimScale Engineering AI Platform

Manufacturer: SimScale GmbH

Product Category: Design Tools & Software

Supporting Documentation (if available)

Engineering simulation has long been a bottleneck in product development — a specialist activity running on expensive on-premise hardware, accessed by the few, completed late in the design cycle, and disconnected from the rest of the engineering workflow. The result: slower iteration, more physical prototypes, and teams making critical design decisions without the physics insight they need.

SimScale is built to change this at the architectural level. As the only cloud-native Engineering AI and Physics AI platform, SimScale gives the entire engineering organization — not just CAE specialists — continuous access to high-fidelity engineering simulation throughout the design cycle. The platform runs entirely in a browser, with no local hardware, no software installation, and no HPC infrastructure required. Engineers set up, run, analyze, and collaborate on simulations from any device, at any scale.

Engineering AI: Bringing simulation to the whole team

SimScale's Engineering AI is not simulation automation — it is an agentic engineering workflow that orchestrates the complete design process, from the statement of engineering intent through to design space exploration, optimization, and report outputs. Engineers define what they want to achieve; SimScale's Engineering AI determines how to get there.

In practice, that means going well beyond automating individual simulation tasks like mesh setup or solver configuration. An engineering team can define a design challenge and have SimScale's agentic workflows orchestrate hundreds of parallel simulation variants, explore the resulting design space, surface optimal configurations, and deliver validated outputs ready for decision-making — without the sequential, manual overhead that makes this scale of exploration impractical in traditional CAE environments.

The impact is measurable across industries. Withings, a health device company, compressed their design-to-prototype cycle by 7x by bringing simulation earlier and broader in the design stage on SimScale. Bellaseno, a medical device manufacturer, eliminated 90% of physical lab prototypes. KSB, a global industrial pump manufacturer, ran a 900-simulation design-of-experiments study in under two days for $300 — a scale of exploration that would have been physically and economically impossible on traditional on-premise hardware.

Physics AI: From simulation data to instant prediction

The second layer is Physics AI — where simulation output becomes a strategic asset. By running high-fidelity simulations at cloud scale, engineering teams can generate the volume and variety of synthetic data needed to train machine learning models that predict performance in milliseconds, not hours.

The clearest example in production comes from Convion, a hydrogen fuel cell and electrolyzer company. Convion faced a highly non-linear design optimization problem: a fluidic device operating at 600°C in harsh chemical environments, where thousands of design variants needed evaluation. Using SimScale, their team ran hundreds of high-fidelity CFD simulations in parallel through a cloud-native design of experiments, then trained a Physics AI model on the results. That model now evaluates new design variants in under an hour — at accuracy within 5% of full-fidelity CFD — and identified a non-intuitive geometry that delivered 50% less physical volume. No conventional optimization approach would have found it.

“The design was complex, and using traditional simulation-driven optimization to find the best-performing configuration would have taken months. We now have an AI model that can generate a new optimized design in under an hour, and I have complete confidence in the results.”
— Armin Narimanzadeh, Manager Thermofluids & Simulations, Convion

Cloud-native architecture and expanding physics breadth

Underpinning both AI layers is SimScale’s cloud-native architecture — built from the ground up for elastic HPC access, real-time collaboration, and compatibility with the modern AI and Digital Twin ecosystem. The platform integrates with NVIDIA PhysicsNeMo for physics-informed AI model development and Omniverse-based tools for Digital Twin environments, enabling teams to connect simulation-generated data directly into downstream AI workflows.

Solver breadth covers the full physics range required by design engineers: CFD (OpenFOAM, LBM, IBM), FEA (Marc), thermal, electromagnetic, and multiphysics. A recent addition is Smoothed Particle Hydrodynamics (SPH) simulation, enhanced through integration of the PAMICS® solver from AI Engineering GmbH. SPH enables meshless simulation of highly dynamic fluid behavior — oil lubrication in powertrain systems, industrial mixing, fluid-structure interactions — with GPU-accelerated runtimes 10–20x faster than traditional grid-based CFD for targeted use cases, and with output that feeds directly into SimScale’s Physics AI and Digital Twin workflows.

The engineering organization of the future

SimScale is trusted by more than 800,000 engineers globally, across industries such as automotive, electronics, industrial equipment, medical devices, energy, and architecture. The consistent outcome across industries is the same: engineers running simulation earlier, exploring more design variants, building fewer prototypes, and making better-informed decisions. Johnson Screens saves $15,000 per experiment by replacing physical testing with SimScale CFD. Bucher Municipal eliminated €40,000 in on-premise hardware costs. Rimac Automobili achieved 20x faster thermal simulation for EV development.
SimScale’s Engineering AI and Physics AI platform represents a genuine architectural shift: from simulation as a late-stage specialist activity to simulation as continuous, team-wide design intelligence — built for the engineering organization of the future.