NVIDIA Modulus
NVIDIA Modulus is an open-source platform for building, optimizing, and training Physics-ML models using a simple Python interface.
Real-time predictions can be made by engineers using Modulus to build AI surrogate models that combine simulation, observed data, and physics-driven causation. By using generative AI with diffusion models, you may enhance engineering simulations and supply higher-fidelity data for scalable, responsive solutions. Modulus can be used to build large-scale digital twin models in a number of physics domains, such as computational fluid dynamics, structural mechanics, and electromagnetics.
Use NVIDIA Modulus to improve your engineering simulations using AI. Models for enterprise-scale digital twin applications can be made in a number of physics domains, such as CFD, structural physics, and electromagnetics.
NVIDIA Modulus: What is it?
NVIDIA Modulus is an open-source platform that allows users to build, train, and optimize physics-machine learning (Physics-ML) models:
What it does: Addresses real-world science and engineering problems using physics-based simulations and AI models.
Developers, researchers, scientists, and businesses are the target audience.
What it is capable of: Help solve problems related to fluid dynamics, materials science, and climate modeling.
How it works: Training data, boundary conditions, and physics are used to build deep learning models.
Innovative model designs, an end-to-end pipeline, and PDE-driven AI techniques are among the features.
NVIDIA Modulus, which is based on PyTorch, is offered under the Apache 2.0 license. It is available via NVIDIA AI Enterprise, an end-to-end AI software platform.
Advantages
Modulus is an open-source, publicly available AI framework that is used to develop cutting-edge AI architectures for physics-ML models and engineering systems.
Toolkit for AI Physics
Quickly configure, build, and train AI models for physical systems in any domain—from engineering simulations to the life sciences by using simple Python APIs.
Customize Models
The NVIDIA NGC library offers advanced pretrained models that may be downloaded, enhanced, and altered.
Deduction in Near Real Time
Create digital twins of your physical systems using AI surrogate models to simulate in nearly real time.
Use NVIDIA AI to Scale Training Performance NVIDIA AI may be used to scale training performance from a single GPU to multi-node implementations.
Open-Source Design
Learn about the benefits of open source. Modulus is built on PyTorch and is available under the Apache 2.0 license.
Uniform
Use the greatest AI development standards to work with physics-ML models, immediately focusing on engineering applications.
Simple to Use
Increase productivity with easy-to-programme Pythonic API interfaces and easily comprehensible error messages.
Outstanding Quality
Use excellent software with enterprise-level development, thorough documentation and validation, and starter tutorials.
Crucial Elements
Novel Model Architectures
Modulus offers a variety of techniques for training physics-based models, ranging from purely physics-driven models like PINNs to physics-based, data-driven architectures like neural operators, GNNs, and generative AI-based diffusion models.
In addition to selected Physics-ML model architectures, Fourier feature networks, Fourier neural operators, and GNNs, Modulus includes diffusion models trained on NVIDIA DGX across free and open-source datasets available in the documentation.
Developing Cutting-Edge Physics-ML Models
Modulus provides an end-to-end pipeline for training Physics-ML models, which includes importing geometry, adding PDEs, and extending the training to multi-node GPUs. Modulus also provides training recipes in the form of reference apps.
Clear Parameterization
Modulus provides clear parameter descriptions for training the surrogate model with a variety of values to learn for the design space and to infer several scenarios simultaneously.
How to Get Started with NVIDIA Modulus
Get it here Development Models and Containers
Use the free Modulus container from NVIDIA NGC and pretrained models to create Physics-ML models.
Large-Scale Operations
Get free access to NVIDIA cloud workflows for Modulus and see how easy it is to scale to enterprise workloads.
Online Self-Study Course
The NVIDIA Deep Learning Institute (DLI) offers a hands-on introductory course that uses Modulus to explore physics-informed machine learning.
Integration of the Omniverse
With an extension that links and creates custom 3D pipelines with the NVIDIA Omniverse platform, the results of a model trained with Modulus may now be viewed. The Modulus addon allows you to import the output results for common output scenarios, such as streamlines and iso-surfaces, into a visualization pipeline. Additionally, it offers an interface that makes it possible to interactively explore design parameters and variables in order to infer new system behavior and visualize it almost instantly.
Production-Ready Solution With NVIDIA AI Enterprise
With NVIDIA AI Enterprise, an end-to-end AI software platform designed to propel businesses to the forefront of AI, Modulus is now accessible. NVIDIA AI Enterprise provides enterprise-grade support, security, and API stability while reducing the possible risks associated with open-source software. It also offers access to AI solution workflows to expedite time to production, validation and integration for NVIDIA AI open-source software, and certifications to deploy AI globally.
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