PhysicsNeMo
Type: Technology Tags: CUDA, NVIDIA, GPU, Physics AI, Scientific Computing, Geoscience, Neural Operators, Simulation, PyG, GNN Related: NVIDIA-Modulus, Earth-2, NIM-for-Earth-2-CorrDiff, NIM-for-Earth-2-FourCastNet, NIM-for-DoMINO-Automotive-Aero, PyTorch, PyG, NVIDIA-DGL, NVIDIA-Warp, cuDNN Sources: NVIDIA official documentation, developer.nvidia.com/physicsnemo, https://docs.nvidia.com/physicsnemo/latest/physicsnemo/examples/dgl_to_pyg_migration.html, https://docs.nvidia.com/nim/earth-2/corrdiff/latest/overview.html, https://docs.nvidia.com/nim/earth-2/fourcastnet/latest/overview.html, https://docs.nvidia.com/nim/physicsnemo/domino-automotive-aero/latest/overview.html Last Updated: 2026-04-30
Summary
PhysicsNeMo (formerly part of Modulus) is NVIDIA’s open-source framework specifically designed for large-scale physics AI model training for geoscience, subsurface modeling, and industrial physics applications. It extends the NVIDIA Modulus physics-ML ecosystem with specialized architectures, datasets, and training workflows for Earth science domains including seismic imaging, weather modeling, reservoir simulation, and ocean dynamics. PhysicsNeMo is the framework powering NVIDIA’s Earth-2 climate AI initiative’s large-scale model training.
Detail
Purpose
PhysicsNeMo addresses the need for scalable, production-grade training of very large physics-AI models (billions of parameters) for geoscience and climate applications, where the models must learn from massive observational and simulation datasets while respecting physical constraints. It provides domain-specific model architectures, data loaders, and multi-node distributed training infrastructure for Earth and physics sciences.
Key Features
- Specialized physics-ML architectures: Spherical Fourier Neural Operator (SFNO), Earth-2 FourCastNet, GraphCast-compatible GNN
- Distributed training across thousands of GPUs for continental/global-scale models
- Support for structured (latitude-longitude grids) and unstructured (mesh) geophysical data
- Data loaders for ERA5, CMIP6, and seismic datasets
- Physics-constrained loss functions for atmospheric, oceanic, and subsurface domains
- Built on PyTorch with Megatron-LM-style distribution for large model training
- PyG / PyTorch Geometric backend support for current and new GNN-based models, with migration guidance from DGL graph objects
- Integration with NVIDIA Earth-2 inference and visualization platform
- Deployment adjacency with NIM-for-Earth-2-CorrDiff and NIM-for-Earth-2-FourCastNet for named Earth-2 inference microservices
- Deployment adjacency with NIM-for-DoMINO-Automotive-Aero for automotive external-aerodynamics surrogate inference.
- Multi-modal training support (combining observational + simulation data)
- Checkpoint compatibility with Modulus training pipelines
Use Cases
- Global weather and climate AI model training (NWP surrogate models)
- Seismic full waveform inversion (FWI) neural surrogates
- Subsurface reservoir simulation with neural operators
- Ocean current and sea surface temperature forecasting
- Climate downscaling (coarse-to-fine resolution enhancement)
- Automotive external-aerodynamics surrogate simulation through NIM-for-DoMINO-Automotive-Aero
- Carbon capture and storage (CCS) subsurface modeling
- Tsunami and flood early warning systems
Hardware Requirements
- NVIDIA A100 or H100 GPUs strongly recommended
- Multi-node clusters required for global-scale model training (1000+ GPUs typical)
- NVLink for intra-node; InfiniBand NDR for inter-node
- CUDA 11.8 or higher
- DGX SuperPOD or equivalent HPC cluster
Language Bindings
- Python (primary)
- YAML/Hydra for experiment configuration
Connections
- NVIDIA-Modulus — PhysicsNeMo is the geoscience-focused evolution of the Modulus framework
- Earth-2 — Earth-2 climate AI platform uses PhysicsNeMo for training its weather/climate models
- NIM-for-Earth-2-CorrDiff - deployable CorrDiff NIM for high-resolution weather downscaling.
- NIM-for-Earth-2-FourCastNet - deployable FourCastNet NIM for global medium-range forecasting.
- NIM-for-DoMINO-Automotive-Aero - deployable PhysicsNeMo NIM for automotive aerodynamic surrogate prediction.
- NVIDIA-Warp — Warp differentiable simulation can be used for adjoint-based data assimilation within PhysicsNeMo
- PyTorch — PhysicsNeMo is built on PyTorch with distributed training extensions
- PyG — PhysicsNeMo documentation recommends PyG for GNN backends and documents migration from DGL to PyG graph objects
- NVIDIA-DGL — legacy DGL context for users migrating existing PhysicsNeMo GNN examples and models toward PyG
- NCCL — multi-node collective communications for distributed physics-ML training