NVIDIA/physicsnemo

Health v2 · maintenance only

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3198 stars · 763 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

NVIDIA PhysicsNeMo is an open-source Python deep-learning framework for building, training, fine-tuning, and inferring physics AI models using state-of-the-art Physics-ML methods. It provides GPU-optimized modules for scalable training pipelines combining physics knowledge with data, supporting neural operators, GNNs, transformers, and Physics-Informed Neural Networks.

Use cases

• train physics-informed neural networks on gpu
• build ai surrogate models for cfd simulation
• train neural operators at scale with pytorch
• develop digital twin models for engineering
• accelerate automotive aerodynamics simulation with ai
• fine-tune physics ai models for structural mechanics

When to choose

• you need GPU-optimized, scalable training of physics-ML models like PINNs, neural operators, or GNNs
• you are building AI surrogate models for engineering simulations such as CFD, structural mechanics, or electromagnetics
• you want a PyTorch-integrated framework with prebuilt model architectures and examples for AI4Science

When to avoid

• your problem has no physics or simulation component and standard deep learning frameworks suffice
• you need a lightweight CPU-only solution, since the stack is optimized for NVIDIA GPUs
• you require a turnkey commercial simulation product rather than a framework for building custom models

Facets

framework · maturity active

machine-learning deep-learning llm-training simulation gpu-computing machine-learning deep-learning simulation artificial-intelligence python cloud physics-informed-neural-networks neural-operators gnn surrogate-models cfd digital-twins pytorch sciml ai4science physics engineering gpu docker linux

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markdown · JSON · MCP: product_card(name="NVIDIA/physicsnemo")