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.