NVIDIA Modulus

NVIDIA Modulus is an open-source framework for building, training, and fine-tuning Physics-ML models with a simple Python interface.

Using Modulus, engineers can build high-fidelity AI surrogate models that blend the causality of physics described by governing partial differential equations (PDEs) with simulation data from CAE solvers or observed data. Such AI models can predict with near-real-time latency and for a parameterized design space.

Using Modulus, you can bolster your engineering simulations with AI. You can build models for enterprise scale digital twin applications across multiple physics domains, from CFD and Structural to Electromagnetics.

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NVIDIA Modulus, a Neural Network Framework

Modulus Data Sheet
NVIDIA Modulus Neural Network Framework


AI toolkit for physics

AI Toolkit for Physics

Configure, build, and train AI models for physical systems quickly with simple Python APIs.

The framework is generalizable to different domains—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems.

Customize models from NVIDIA NGC catalog

Customize Models

Download and customize pretrained state-of-the-art models from the NVIDIA NGC? catalog. Build on the reference applications and extend it to your use case.

Deploy and simulate AI surrogate models in real time

Near-Real-Time Inference

Deploy AI surrogate models as digital twins of your physical systems to simulate in near real time.

Scale training performance with NVIDIA AI

Scale With NVIDIA AI

Leverage NVIDIA AI to scale training performance from single-GPU to multi-node implementations.

See Modulus in Action

Accelerating Extreme Weather Prediction with FourCastNet

Siemens Energy HRSG Digital Twin Simulation Using NVIDIA Modulus and Omniverse

Maximizing Wind Energy Production Using Wake Optimization

Key Features

New Model Architectures

Modulus offers a variety of approaches for training physics-based models, from purely physics-driven models like PINNs to physics-based, data-driven architectures such as neural operators.

Modulus includes curated Physics-ML model architectures, Fourier feature networks, or Fourier neural operators trained on NVIDIA DGX across open-source, free datasets found in the documentation.

Training State-of-the-Art Physics-ML Models

Modulus provides an end-to-end pipeline for training Physics-ML models—from ingesting geometry to adding PDEs and scaling the training to multi-node GPUs. Modulus also includes training recipes in the form of reference applications.

Explicit Parameterization

Modulus provides explicit parameter specifications for training the surrogate model with a range of values to learn for the design space and for inferring multiple scenarios simultaneously.


Omniverse Integration

Modulus is now integrated with NVIDIA Omniverse? via the Modulus extension that can be used to visualize the outputs of a Modulus-trained model. The extension enables you to import the output results into a visualization pipeline for common output scenarios, such as streamlines and iso-surfaces. It also provides an interface that enables interactive exploration of design variables and parameters for inferring new system behavior and visualizing it in near real time.

Omniverse Integration Documentation

Ways to Get Started With NVIDIA Modulus

Download Containers and Models for Development

Download Containers and Models for Development

Develop Physics-ML models using Modulus container and pretrained models, available for free on NVIDIA NGC.

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Enterprise-Scale Workflows

Enterprise-Scale Workflows

Get free access to NVIDIA cloud workflows for Modulus and experience the ease of scaling to enterprise workloads.

Try on NVIDIA LaunchPad
Self-Paced Online Course

Self-Paced Online Course

Take a hands-on introductory course from the NVIDIA Deep Learning Institute (DLI) to explore physics-informed machine learning with Modulus.

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What Others Are Saying

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Using NVIDIA Modulus and Omniverse Wind Farm Digital Twin for Siemens Gamesa

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Siemens Energy Taps NVIDIA to Develop Industrial Digital twin of Power Plant in Omniverse and Modulus

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Developing Digital Twins for Weather, Climate, and Energy

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Modulus Featured Content

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