Audience

Researchers, engineers, and developers who need an open source Python AI framework to build, train, fine-tune, and deploy physics-informed machine learning models for simulation, digital twins, and real-time prediction

About NVIDIA PhysicsNeMo

NVIDIA PhysicsNeMo is an open source Python deep-learning framework for building, training, fine-tuning, and inferring physics-AI models that combine physics knowledge with data to accelerate simulations, create high-fidelity surrogate models, and enable near-real-time predictions across domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather and climate, and digital twin applications. It provides scalable, GPU-accelerated tools and Python APIs built on PyTorch and released under the Apache 2.0 license, offering curated model architectures including physics-informed neural networks, neural operators, graph neural networks, and generative AI–based approaches so developers can harness physics-driven causality alongside observed data for engineering-grade modeling. PhysicsNeMo includes end-to-end training pipelines from geometry ingestion to differential equations, reference application recipes to jump-start workflows.

Pricing

Starting Price:
Free
Free Version:
Free Version available.

Integrations

API:
Yes, NVIDIA PhysicsNeMo offers API access

Ratings/Reviews

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Company Information

NVIDIA
Founded: 1993
United States
developer.nvidia.com/physicsnemo

Videos and Screen Captures

NVIDIA PhysicsNeMo Screenshot 1
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Product Details

Platforms Supported
Cloud
Training
Documentation
Live Online
Webinars
Videos
Support
Phone Support
Online

NVIDIA PhysicsNeMo Frequently Asked Questions

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Q: Does NVIDIA PhysicsNeMo have an API?
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NVIDIA PhysicsNeMo Product Features