Welcome to the NVIDIA PhysicsNeMo blog! Here you'll find the latest insights, tutorials, and updates about the PhysicsNeMo framework. Discover how neural operators, GNNs, and generative AI models can enhance your engineering simulations and workflows.
For a developer, the journey from working Python scripts to a production-ready cloud solution is often paved with "infrastructure tax." For instance, as a developer you’ve cracked the problem of applying AI models for your weather related application using powerful libraries like Earth2Studio.
PhysicsNeMo-Mesh is a GPU-accelerated mesh processing module, built on PyTorch and TensorDict, and included in the open-source PhysicsNeMo library. It provides a) a GPU-native, high-performance mesh data structure with design choices that are particularly well-suited for ML workflows, and b) a diverse suite of accelerated mesh operations that can be used to bridge existing gaps between data preprocessing and training/inference.
Its native data format allows loading meshes from disk much more quickly than VTU (9x-88x faster in our testing; hardware-dependent) while preserving the full mesh structure that flat tensor formats like zarr typically discard. This gives you the speed you need for training while retaining the geometric and physical context needed for model- and dataset-agnostic workflows. In this post, we'll show what PhysicsNeMo-Mesh can do for your training pipeline.
Simulating automotive crash is one of the most complex computational workloads in computer-aided engineering (CAE). Crash simulation is a highly non-linear, dynamic event that happens in milliseconds. Therefore automotive crash simulations face significant bottlenecks in both human-driven workflows and raw computational demands. These challenges stem from the need to model highly non-linear, split-second events with extreme accuracy to meet strict regulatory standards (like NHTSA or Euro NCAP).
DoMINO is one of the most popular and accurate models in PhysicsNeMo, with top accuracy metrics as measured by physicsnemo-cfd. Originally developed by and for PhysicsNeMo, DoMINO has been overhauled for performance optimizations and scale out enhancements. In this blog post, we'll highlight the performance enhancements we've made to DoMINO - giving more than 30x end to end speed up on DrivAerML training - as well as how you can use them from PhysicsNeMo for your own models.
Transolver, a highlight at ICML 2024 and a leading CFD surrogate model, is now significantly enhanced within PhysicsNeMo. Originally integrated by co-author Huakun Luo, these updates empower AI-Physics developers to rapidly prototype, scale, and apply surrogate models to enterprise applications like automotive aerodynamics.