Our Agentic Physics AI Platform: your innovation engine for autonomous engineering.
One platform for the full engineering loop, across multiple physics domains. AI agents set up and run simulations, train Physics AI models on your engineering data, and put pretrained foundation models to work. Specialized design optimization workflows, and custom workflows built around your process, turn every result into a better design.
See our platform in action.
Platform capabilities.
Each capability is marked with where it stands today, so you know what you can put to work now and what is still in development.
Agentic Physics AI
Turn the engineering data you already have into fast, physics-aware AI models. Agents handle data preparation, training, fitting, and inference, with NVIDIA PhysicsNeMo built in, so a new model is a workflow away, not a research project.
How it works
- Engineering data
- Physics AI agent
- Training and fitting
- Inference
- Physics AI model
Built on your data
Simulation output, test results, and measurements become models tuned to your products.
Agents do the heavy lifting
Data preparation, training, and inference run as one managed workflow, with no bespoke scripts.
Checked against the physics
Predictions are tested for physical consistency, not just a good fit score.
Supports
- NVIDIA PhysicsNeMo
- Simulation data
- Test data
- Sensor data
Foundation Physics Models
Pretrained on large corpora of meshes and simulation outputs, foundation models predict physical fields out of the box, with no per-problem training campaign. Run open-source models on the platform today while we build our own.
How it works
- Mesh, geometry, boundary conditions, parameters
- Physics foundation model
- Physical field prediction
Predictions out of the box
Fast field estimates without generating a new training dataset first.
Made to generalize
Designed to work across geometries, mesh resolutions, and operating conditions.
Open models now, ours next
Open-source foundation models run on the platform today; our own model is in development.
Agentic Design Optimization: from engineering intent to engineering decision.
Bring your design files and describe the goal. Agents set up the study, run simulations automatically, train physics AI models on the results, and use them to search the design space, then hand you the parameter values for the best design with the evidence behind them.
Understand the Request
Describe the goal in plain language. Agents read your design files, identify the design parameters, their ranges, and the objective, and confirm the setup with you before anything runs.
Select Optimization Settings
Agents choose the study strategy, simulation budget, and physics AI training settings that fit the problem, so there is nothing to hand-tune.
Explore the Design Space
A broad first set of design variants maps the design space, so promising regions are not missed.
Run Simulations
Simulations launch automatically on cloud infrastructure, stay within your compute budget, and have their results extracted as they finish.
Train Physics AI Models
Completed simulations become training data for physics AI models that predict full-field results, not just a single number.
Learn and Refine
Every round of results informs the next, so new simulations go where they add the most insight.
Optimize the Design
The trained models evaluate candidate designs in seconds, searching far more of the design space than simulation alone could afford.
Validate the Best Design
The strongest candidates are confirmed with the solver, and you get the parameter values for the best design with every step on the record.
Far fewer simulations
Reach a better design with a fraction of the simulations a conventional study needs.
Hands-off, end to end
Agents pick the settings, run the simulations, and train the models. You confirm the setup and review the result.
Decisions you can defend
The best design is confirmed with the solver, and every step keeps the configuration and results behind it.
Supports
- Python design scripts
- Physics AI models
- NVIDIA PhysicsNeMo
- OpenRadioss
Agentic Simulation Automation
Describe the simulation in plain language. An agent configures the case, runs it on AWS cloud infrastructure, and returns results ready to inspect, with no solvers to install and no cluster to maintain.
How it works
- Describe the simulation
- AI agent
- Simulation workflow
- Cloud execution
- Results
- Visualization
Plain-language setup
Brief it like a colleague; the agent turns your request into a runnable solver case.
End-to-end automation
Meshing, setup, solving, and post-processing run as one recorded, repeatable workflow.
Elastic AWS compute
Compute spins up for each run and shuts down after. Nothing to size, patch, or maintain.
Supports
- OpenRadioss · explicit dynamics
- OpenFOAM · CFD
- AWS cloud
Engineering Visualization
See results where they happen: fields, contours, and animations rendered on the geometry itself, with interactive plots alongside. Rotate, zoom, step through time, and query any value.
How it works
- Open the model or result
- 3D viewer
- Mesh and contours
- Animation
- Interactive plots
Results on the geometry
Fields render on the component, so you see where a problem occurs, not just that it does.
Built for real meshes
Large, irregular engineering meshes render without smoothing away the gradients that matter.
Visual and quantitative
Interactive plots carry the numbers behind every contour, in the same session.
Supports
- 3D mesh rendering
- Field contours
- Transient animation
- Interactive plots
Domain Expert SLMs
Fine-tune small language models on your domain knowledge to create experts that power our agents. They speak your discipline's language, respond faster, and cut LLM costs across every workflow.
How it works
- Domain knowledge
- Curation agent
- Fine-tuning
- Domain evaluation
- Domain expert SLM
Expertise in your field
Trained on the material that matters to your discipline, not generic web text.
Secure, on-prem expertise
Compact SLMs run on your own network, so your data never leaves it. LoRA adapters switch between domain experts in seconds.
Lower LLM costs
Compact models serve faster and cheaper than large general-purpose LLMs.
The governing physics and analysis types the platform covers.
Every physical phenomenon is described by governing equations: partial differential equations that express the conservation of mass, momentum, and energy. Navier–Stokes governs fluid flow, the heat equation governs conduction, and the equations of motion govern how solids deform. The equations differ from domain to domain, but the way we solve them does not: identify the governing physics, define the geometry, materials, and boundary and initial conditions, discretize and solve numerically, then verify the result before it drives a design decision. The domains differ; the discipline does not.
Thermal Analysis
Understand how heat moves through components, assemblies, and systems.
Conjugate Heat Transfer Analysis
Model coupled solid conduction and fluid convection together.
CFD
Simulate fluid flow, pressure, and turbulence around and through geometry.
Structural Static / Dynamic Analysis
From static stress and stiffness to transient, explicit, and impact or crash loading.
Run the platform on your own problem.
Open the platform if you already have access, or request a demo and our team will walk you through it.