physicsnemo and physicsnemo-sym
PhysicsNeMo-Sym is a specialized extension layer that builds on top of PhysicsNeMo core, providing higher-level domain-specific abstractions and physics-informed training utilities, making them complements designed to be used together rather than alternatives.
About physicsnemo
NVIDIA/physicsnemo
Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods
Provides modular PyTorch-compatible building blocks including neural operators (FNOs, DeepONet), graph neural networks, and physics-informed layers, with built-in datapipes for scientific data structures like point clouds and meshes. Features GPU-optimized distributed training utilities that scale from single to multi-node clusters, plus domain-specific sub-modules for CFD, data curation, and weather/climate applications.
About physicsnemo-sym
NVIDIA/physicsnemo-sym
Framework providing pythonic APIs, algorithms and utilities to be used with PhysicsNeMo core to physics inform model training as well as higher level abstraction for domain experts
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