Kit-CAE = Extension platform that bridges the Omniverse ecosystem
3) Usage Patterns
CAE Data – Simulation results from tools such as Ansys, Star-CCM+, or OpenFOAM.
Curator – Converts data into AI-training-ready formats.
Physics NeMo – Trains AI surrogate models from converted data.
NIM – Deploys trained models as APIs for external access.
Omniverse – Uses Kit-CAE to fetch API results for real-time 3D visualization within digital-twin environments.
A. Comparison & Analysis Mode (for Researchers / Analysts)
Export analysis result files (.cgns, .vtk, .npz) from existing CAE tools (Ansys, Star-CCM+, OpenFOAM etc.).
Import and visualize them in Kit-CAE.
Retrieve inference results from AI surrogate models deployed via NIM API calls.
Compare traditional analysis and AI prediction results side-by-side in the same Omniverse scene.
Context
Researchers and developers can intuitively compare actual analysis data against AI predictions.
Enables rapid design evaluation and model validation without repetitive experiments.
B. Real-Time Digital-Twin Mode (for Operations / Simulation)
rain a Physics NeMo surrogate model on existing analysis data and deploy it through NIM API.
The AI model immediately returns physical analysis outputs (flow fields, stress, temperature distribution, etc.).
In apps like Isaac Sim, real-time state data (velocity, position, temperature, etc.) from robots or equipment are sent to the NIM API.
Kit-CAE visualizes these results on the USD scene, providing instant feedback.
Context
Manufacturing digital twin – Monitor thermal stress and deformation in real time during operation.
Logistics simulation – Predict and display airflow or temperature changes instantly as AMRs move.
Robotics & AI training – Reinforcement learning within simulation including a physical feedback loop.
Flow NanoVDB
concept_car.npz
The Kit-CAE examples (e.g., Streamlines, Volume Rendering, etc.) visualize existing CAE analysis data in real time, producing an animation that appears as dynamically moving fluid.
Dynamic Visualization (Without AI Computation)
Streamline and Flow visualizations are based on existing analysis data (e.g., velocity vector fields) and update paths in real time.
For instance, when you move the seed sphere, streamlines update instantly to make the fluid flow appear animated.
However, this is not a simulation run (AI or physics computation) but a purely visual update.
Difference from AI-Based Dynamic Prediction
Function
Description
Visualization inside Kit-CAE
Interactively manipulate streamlines/volumes generated from existing analysis data in real time.
AI Surrogate Model (Physics NeMo → NIM)
A separate workflow that trains an AI model on analysis data → deploys an API → runs actual predictions. Not included in the default Kit-CAE examples.
