Physicsnemo

PhysicsNeMo

Overview

Open-source Python framework for building, training, and fine-tuning physics-informed AI models on NVIDIA GPUs. PhysicsNeMo provides optimized model architectures (FNO, AFNO, GNNs, diffusion models), scalable distributed training, and physics-constrained learning — enabling real-time AI surrogates for scientific simulation across weather, CFD, molecular dynamics, and engineering domains.

Quick Pattern

Incorrect — manual PyTorch training without physics optimization:

model = MyModel().cuda()
for batch in dataloader:
    pred = model(batch)
    loss = F.mse_loss(pred, target)
    loss.backward()

Correct — PhysicsNeMo with optimized training and CUDA graphs:

import physicsnemo
from physicsnemo.datapipes.benchmarks.darcy import Darcy2D
from physicsnemo.metrics.general.mse import mse
from physicsnemo.models.fno.fno import FNO

model = FNO(
    in_channels=1, out_channels=1,
    dimension=2, latent_channels=32,
    num_fno_layers=4, num_fno_modes=12,
    padding=5,
).to("cuda")

optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
dataloader = Darcy2D(resolution=256, batch_size=64)

for batch in dataloader:
    pred = model(batch["permeability"])
    loss = mse(pred, batch["darcy"])
    loss.backward()
    optimizer.step()

Quick Command

# Install PhysicsNeMo
pip install nvidia-physicsnemo

# Install with all optional dependencies
pip install nvidia-physicsnemo[all]

# Install PhysicsNeMo-Sym (physics-informed constraints)
pip install Cython
pip install nvidia-physicsnemo-sym --no-build-isolation

# Run with Docker container
docker pull nvcr.io/nvidia/physicsnemo/physicsnemo:latest
docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \
  --runtime nvidia -v ${PWD}:/workspace \
  -it --rm nvcr.io/nvidia/physicsnemo/physicsnemo:latest bash

# Distributed training
torchrun --nproc_per_node=4 train.py

# Clone training recipes
git clone https://github.com/NVIDIA/physicsnemo.git

Quick Reference

Built-in Model Architectures

| Model | Module | Domain | |-------|--------|--------| | FNO | physicsnemo.models.fno.fno.FNO | General PDE solving | | AFNO | physicsnemo.models.afno.afno.AFNO | Weather forecasting (FourCastNet) | | GraphCast | physicsnemo.models.graphcast | Global weather prediction | | MeshGraphNet | physicsnemo.models.meshgraphnet | Mesh-based simulation | | X-MeshGraphNet | physicsnemo.models.meshgraphnet | Large-scale mesh (100M+ cells) | | Hybrid MeshGraphNet | physicsnemo.models.meshgraphnet | Complex boundary conditions | | SFNO | physicsnemo.models.sfno | Weather on lat-lon grids | | DeepONet | physicsnemo.models.deeponet | Operator learning (branch+trunk) | | Pix2Pix | physicsnemo.models.pix2pix | Image-to-image physics | | DLWP | physicsnemo.models.dlwp | Weather prediction | | DoMINO | physicsnemo.models.domino | Aerodynamics, point-cloud CFD | | Transolver | physicsnemo.models.transolver | General PDE, fp8 on Hopper | | DPOT | physicsnemo.models.dpot | Pre-trained operator transformer | | SRResNet | physicsnemo.models.srrn | Super-resolution | | RNN | physicsnemo.models.rnn | Transient physics | | FigConvNet/UNet | physicsnemo.models.figconvnet | External aerodynamics | | 3D UNet | physicsnemo.models.unet | Voxel-based 3D simulation | | FullyConnected | physicsnemo.models.mlp.fully_connected | General MLP | | Diffusion UNets | physicsnemo.models.diffusion_unets | Downscaling, FWI, topology opt |

Core Utilities

| Component | Import | Purpose | |-----------|--------|---------| | Distributed | physicsnemo.distributed.DistributedManager | Multi-GPU orchestration | | ShardTensor | physicsnemo.distributed.scatter_tensor | Domain parallelism | | CUDA Graphs | physicsnemo.utils.StaticCaptureTraining | Training optimization | | PhysicsInformer | physicsnemo.utils.physics_informer.PhysicsInformer | PDE residual computation | | Metrics | physicsnemo.metrics.general.mse | Loss computation | | Datapipes | physicsnemo.datapipes.benchmarks.* | Scientific data loading | | Curator | physicsnemo_curator.etl.* | GPU-accelerated data curation | | Module | physicsnemo.models.module.Module | Base model with checkpoint support | | Warp Neighbors | physicsnemo.utils.neighbors | GPU ball query for point clouds | | Logging | physicsnemo.launch.logging.LaunchLogger | MLflow / W&B integration | | Active Learning | physicsnemo.active_learning.driver.Driver | Iterative model improvement |

Hardware Requirements

| GPU Family | Examples | Notes | |-----------|----------|-------| | Blackwell | B100, RTX PRO 6000 | Latest, full support | | Hopper | H100 | Recommended, fp8 via TE | | Ada Lovelace | RTX 40xx | Supported | | Ampere | A100, A30, A6000 | Recommended for training | | Turing | T4 | Inference focused | | ARM64 | — | Supported |

OS: Ubuntu 24.04 (WSL supported). Python >= 3.10. Docker: nvcr.io/nvidia/physicsnemo/physicsnemo:

Domain-Specific Packages

| Package | Purpose | Install | |---------|---------|---------| | PhysicsNeMo-CFD | CFD inference (DoMINO NIM) | physicsnemo.cfd | | PhysicsNeMo Curator | Data curation ETL | physicsnemo-curator | | Earth2Studio | Weather/climate inference | earth2studio |

Example Applications

| Application | Domain | Key Models | |-------------|--------|------------| | FourCastNet (AFNO) | Global weather | AFNO at 0.25deg resolution | | CorrDiff | Km-scale downscaling | Diffusion + multi-diffusion | | StormCast | Convection-allowing weather | Diffusion UNet, EDM sampler | | GraphCast | Medium-range weather | Distributed GNN | | DoMINO Aerodynamics | External CFD | Multi-scale neural operator | | XAeroNet | External aerodynamics | Surface + volume prediction | | Crash Dynamics | Structural mechanics | X-MeshGraphNet (v25.11) | | Datacenter Thermal | Digital twin | 3D UNet, physics-informed FD | | FWI Geophysics | Subsurface inversion | Diffusion + DPS | | TopoDiff | Design optimization | Diffusion + manufacturability | | Darcy Flow | CFD benchmark | FNO, Transolver, DeepONet | | Blood Flow 1D | Healthcare | MeshGraphNet reduced-order | | Helmholtz | Wave equations | PhysicsNeMo-Sym PINN |

When to Apply

Reference these guidelines when:

• Training neural operators (FNO, AFNO, DoMINO) for PDE solving
• Building weather/climate AI models (GraphCast, CorrDiff, StormCast)
• Creating physics-informed neural networks (PINNs)
• Scaling scientific AI training to multi-GPU clusters
• Developing AI surrogates for CFD, structural mechanics, or engineering simulation
• Converting PyTorch models to PhysicsNeMo for optimization
• Deploying physics-AI models via NIM or Earth2Studio
• Curating engineering datasets with GPU-accelerated ETL pipelines
• Integrating external kernels (cuML, Warp) with torch.compile
• Migrating GNN code from DGL to PyTorch Geometric

Priority-Ordered Guidelines

| Priority | Category | Impact | Prefix | |----------|----------|--------|--------| | 1 | Training Recipes | CRITICAL | training-* | | 2 | Model Architectures | CRITICAL | models-* | | 3 | Distributed Training | HIGH | distributed-* | | 4 | Data Pipelines | HIGH | data-* | | 5 | Advanced Features | HIGH | advanced-* | | 6 | Deployment | MEDIUM | deployment-* | | 7 | Physics Constraints | MEDIUM | physics-* |

References

Full documentation with code examples in [references/](references/):

| File | Impact | Description | |------|--------|-------------| | [training-recipes.md][training-recipes] | CRITICAL | FNO training, CUDA graphs, optimizer setup, loss functions | | [models-and-architectures.md][models-and-architectures] | CRITICAL | 18+ architectures: GNNs, transformers, neural operators, diffusion, voxel | | [distributed-training.md][distributed-training] | HIGH | DistributedManager, DDP, multi-node, torchrun patterns | | [data-pipelines.md][data-pipelines] | HIGH | Datapipes, normalization, Curator ETL, ERA5 download | | [advanced-features.md][advanced-features] | HIGH | PhysicsInformer, torch.compile, Warp/TE layers, DGL→PyG, active learning | | [deployment.md][deployment] | MEDIUM | Checkpointing, DoMINO NIM, Earth2Studio, ONNX export | | [physics-constraints.md][physics-constraints] | MEDIUM | PhysicsNeMo-Sym, PINNs, custom physics losses, Key API |

Problem -> Skill Mapping

| Problem | Start With | |---------|------------| | Train a neural operator | [training-recipes.md][training-recipes] | | Choose a model architecture | [models-and-architectures.md][models-and-architectures] | | Scale to multiple GPUs | [distributed-training.md][distributed-training] | | Load scientific data | [data-pipelines.md][data-pipelines] | | Deploy trained model | [deployment.md][deployment] | | Add physics constraints | [physics-constraints.md][physics-constraints] | | Build weather AI model | [models-and-architectures.md][models-and-architectures] -> [training-recipes.md][training-recipes] | | Convert PyTorch model | [models-and-architectures.md][models-and-architectures] | | Optimize training speed | [training-recipes.md][training-recipes] -> [distributed-training.md][distributed-training] | | Add physics loss to data model | [advanced-features.md][advanced-features] | | Active learning loop | [advanced-features.md][advanced-features] | | Domain decomposition | [advanced-features.md][advanced-features] | | Experiment tracking | [advanced-features.md][advanced-features] | | Evaluate weather model | [advanced-features.md][advanced-features] | | Curate engineering datasets | [data-pipelines.md][data-pipelines] | | Run DoMINO NIM inference | [deployment.md][deployment] | | Run weather forecast with AI | [deployment.md][deployment] | | Integrate cuML/Warp with torch.compile | [advanced-features.md][advanced-features] | | Migrate DGL to PyG | [advanced-features.md][advanced-features] | | Simulate 100M+ cell mesh | [models-and-architectures.md][models-and-architectures] -> [distributed-training.md][distributed-training] | | Topology optimization | [models-and-architectures.md][models-and-architectures] | | Full waveform inversion | [models-and-architectures.md][models-and-architectures] |

[training-recipes]: references/training-recipes.md [models-and-architectures]: references/models-and-architectures.md [distributed-training]: references/distributed-training.md [data-pipelines]: references/data-pipelines.md [deployment]: references/deployment.md [advanced-features]: references/advanced-features.md [physics-constraints]: references/physics-constraints.md

Attribution

Based on NVIDIA PhysicsNeMo documentation and physicsnemo repository.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: dtunai
• Source: dtunai/agent-skills-for-compute
• License: MIT

Install and usage instructions live in the source repository linked above.