NVIDIA PhysicsNeMo
NVIDIA PhysicsNeMo is an open-source Python framework for building, training, and fine-tuning physics AI models at scale.
NVIDIA PhysicsNeMo provides utilities that enable developers to build AI surrogate models that combine physics-driven causality with simulation and observed data, enabling real-time predictions. From neural operators and GNNs to generative AI models, developers can develop proprietary AI models to enhance engineering simulations and generate higher-fidelity data for scalable, responsive designs. NVIDIA PhysicsNeMo supports the creation and validation of large-scale digital twin models across various physics domains, from computational fluid dynamics and structural mechanics to electromagnetics.
Use NVIDIA PhysicsNeMo to bolster your engineering simulations with AI. You can build and validate models for enterprise-scale digital twin applications across multiple physics domains, from computational fluid dynamics and structural to electromagnetics.
Download ContainerDocumentationNVIDIA PhysicsNeMo GitHubData Sheet
Physics-Informed Machine Learning for Surrogate Models
What’s New In NVIDIA PhysicsNeMo
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Features
PhysicsAI Model Architectures
NVIDIA PhysicsNeMo offers a variety of approaches tuned for training physics AI models, from purely physics-driven models like physics-informed neural networks (PINNs) to physics-based, data-driven architectures, such as neural operators, graph neural networks (GNNs), and generative AI-based diffusion models.
NVIDIA PhysicsNeMo includes curated physics-ML model architectures, Fourier feature networks, Fourier neural operators, GNNs, point cloud and diffusion models trained on NVIDIA DGX™ across open-source, free datasets found in the documentation.
Training State-of-the-Art Physics AI Models
NVIDIA PhysicsNeMo provides an end-to-end pipeline and utilities for training physics-ML models—from ingesting geometry to adding partial differential equations. NVIDIA PhysicsNeMo also includes training recipes in the form of reference applications. View documentation.
Training at Scale
NVIDIA PhysicsNeMo provides GPU accelerated distributed framework to build foundational scale models. It provides data parallel and model parallel training pipelines scaled to multi-node training to suit the needs of industrial scale problems. For instance, developers can train a GNN or a point cloud on a 50 million node mesh quite easily. View documentation.
Physics AI Reference Pipelines
NVIDIA PhysicsNeMo offers a diverse set of reference pipelines as a starting point for developers to customize and build their own solutions. These reference samples show how one can model a specific problem with AI as well as showcase some relevant architectures and datasets. This spans use cases from computational fluid dynamics (CFD) and thermal analysis to climate and weather. View the GitHub repo for the complete list.
Benefits
NVIDIA PhysicsNeMo is an open-source, freely available AI framework for developing physics-ML models and novel AI architectures for engineering systems.
AI Toolkit for Physics
Quickly configure, build, and train AI models for physical systems in any domain, from engineering simulations to life sciences, with simple Python APIs.
Customize Models
Download, build on, and customize state-of-the-art pretrained models from the NVIDIA NGC™ catalog.
Near-Real-Time Inference
Deploy AI surrogate models as digital twins of your physical systems to simulate in near real time.
Scale With NVIDIA AI
Leverage NVIDIA AI to scale training performance from a single GPU to multi-node implementations.
Open-Source Design
Experience the benefits of open source. PhysicsNeMo is built on top of PyTorch and is released under the Apache 2.0 license.
Standardized
Work with the best practices of AI development for physics-ML models, with an immediate focus on engineering applications.
User Friendly
Boost productivity with user-comprehensible error messages and easy-to-program Pythonic API interfaces.
High Quality
Use high-quality software with enterprise-grade development, tutorials for getting started, and robust validation and documentation.
Contribute to NVIDIA PhysicsNeMo Development
NVIDIA PhysicsNeMo provides a unique platform for collaboration within the scientific community. Domain experts are invited to contribute and accelerate physics-ML across a variety of use cases and applications.
See NVIDIA PhysicsNeMo in Action
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Explore NVIDIA PhysicsNeMo Examples
Ways to Get Started With NVIDIA PhysicsNeMo
PhysicsNeMo Framework
NVIDIA PhysicsNeMo provides utilities to build Physics AI solutions combining physics with data at enterprise scale.
Computer-Aided Engineering (CAE)
Explore how NVIDIA is enabling computer-aided engineering industry developers to accelerate physics-based CAE simulations and embrace real-time interactive design using AI-accelerated digital twins.
External Aerodynamics NIM
This very accurate pretrained model based on DoMINO architecture and trained on a large and diverse automotive dataset is packaged as an NVIDIA NIM™ microservice for performant and scalable cloud deployment. Developers can customize the model further using the training recipe in PhysicsNeMo.
Weather and Climate (Earth-2)
Explore how NVIDIA is enabling the weather and climate ecosystem for better predictions and disaster mitigation leveraging microservices.
Containers and Models for Development
Develop physics-ML models using NVIDIA PhysicsNeMo container and pretrained models, available for free on NVIDIA NGC.
Enterprise-Scale Workflows
NVIDIA PhysicsNeMo is available with NVIDIA AI Enterprise, an end-to-end AI software platform optimized to accelerate an enterprise’s time to production, certifications to deploy AI everywhere, and enterprise-grade support, security, and API stability while mitigating the potential risks of open-source software.
Higher Education and Research Developer Resources
Open Hackathons and Bootcamps
Accelerate and optimize research applications with mentors by your side.
Teaching Kit for Educators
A DLI Teaching Kit is available to qualified university educators interested in physics-ML. Comprehensive and modular, the kit can help you integrate lecture materials, hands-on exercises, GPU cloud resources, and more into your curriculum.
Self-Paced Online Course
Take a hands-on introductory course from the NVIDIA DLI to explore physics-informed machine learning with NVIDIA PhysicsNeMo.
Access CourseExplore More Resources
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Ethical AI
NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.