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Introducing PhysicsNeMo-Mesh: GPU-Accelerated Mesh Processing for Scientific ML

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.

Building AI Surrogate Models for Structural and Automotive Crash Analysis with NVIDIA PhysicsNeMo

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).

This post explains how to build an AI surrogate model for structural and automotive crash analysis with NVIDIA PhysicsNeMo, from finite element data curation and architecture selection through temporal training and validation.

Optimizing and Scaling DoMINO

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.

Accelerating AI Physics development with PyTorch and PhysicsNeMo

This tutorial blog is designed for AI researchers and scientific machine learning (SciML) developers who wish to leverage PhysicsNeMo in creating AI Physics models. NVIDIA PhysicsNeMo is built upon PyTorch, the leading standard for AI model development. Organizations can utilize the native PyTorch stack and PhysicsNeMo to go from proof-of-concept development to production-scale training and inference workflows, achieving significant performance and operational efficiencies through enterprise-hardened PhysicsNeMo modules. This blog will demonstrate how AI researchers and SciML developers can progressively integrate PhysicsNeMo modules into their PyTorch stack.