NVIDIA PhysicsNeMo

NVIDIA PhysicsNeMo is an open-source PyTorch framework for physics machine learning (physics ML), scientific machine learning (SciML), and AI for science and engineering. It provides reusable library components and end-to-end training recipes.

pip install "nvidia-physicsnemo[cu13]"

For CUDA 12, a basic install, optional features, or source setup, refer to installation options.

PhysicsNeMo in Action

Every visual above comes from a PhysicsNeMo recipe or workflow.

Why PhysicsNeMo

• PyTorch-native and composable. Use a complete architecture, a layer, a numerical operator, or a data transform without replacing your existing PyTorch workflow.
• Built for scientific representations. Work with regular grids, meshes, point clouds, graphs, and nested physical fields while preserving their structure.
• Scale the sample itself. Use ShardTensor domain parallelism to split a single high-resolution sample across GPUs, alongside PyTorch DistributedDataParallel (DDP) or Fully Sharded Data Parallel 2 (FSDP2).

Explore Recipes

Start from a recipe and adapt it to your data, geometry, physics, and deployment constraints. Explore by physical domain:

• ✈️ Engineering design and CFD: train and compare current surface and volume models with the unified external-aerodynamics recipe or accelerate data-center airflow.
• 🌦️ Weather, climate, and water: build global forecasts from the weather recipes or predict floods.
• 🏗️ Structures and manufacturing: emulate deforming structures.
• 🌍 Geophysics and subsurface systems: build reservoir surrogates.
• 🫀 Healthcare: perform brain anomaly detection.
• ✨ Generative and inverse physics: compose the diffusion toolkit with topology-generation recipes.
• 🔁 Simulation-data loops: select new simulations with active learning.

Browse the complete example catalog for every available recipe.

Explore the PhysicsNeMo Ecosystem

PhysicsNeMo supplies reusable models, training components, and recipes. For a domain-focused application layer or data preparation, continue with:

Need	Continue with
Scientific data preparation	PhysicsNeMo Curator for extract, transform, and load (ETL) pipelines that prepare AI-ready scientific and engineering datasets
Engineering inference and design	PhysicsNeMo CFD for inference, evaluation, benchmarking, and design workflows
Weather and climate	Earth2Studio for building and deploying AI weather and climate workflows
Atomistic simulation	NVIDIA ALCHEMI Toolkit for GPU-first training, inference, and dynamics workflows, with optimized primitives from ALCHEMI Toolkit Ops

Choose a Model Family

PhysicsNeMo models are ordinary torch.nn.Module objects. Choose a family by the representation of your data and the task you need to solve. Model names link to source code. The final column links to examples and papers.

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