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What is NVIDIA Modulus?

NVIDIA Modulus is a sophisticated neural network framework designed to seamlessly combine the principles of physics, encapsulated through governing partial differential equations (PDEs), with data to develop accurate, parameterized surrogate models that deliver near-instantaneous responses. This framework is particularly suited for individuals tackling AI-driven physics challenges or those creating digital twin models to manage complex non-linear, multi-physics systems, ensuring comprehensive assistance throughout their endeavors. It offers vital elements for developing physics-oriented machine learning surrogate models that adeptly integrate physical laws with empirical data insights. Its adaptability makes it relevant across numerous domains, such as engineering simulations and life sciences, while supporting both forward simulations and inverse/data assimilation tasks. Moreover, NVIDIA Modulus facilitates parameterized representations of systems capable of addressing various scenarios in real time, allowing users to conduct offline training once and then execute real-time inference multiple times. By doing so, it empowers both researchers and engineers to discover innovative solutions across a wide range of intricate problems with remarkable efficiency, ultimately pushing the boundaries of what's achievable in their respective fields. As a result, this framework stands as a transformative tool for advancing the integration of AI in the understanding and simulation of physical phenomena.

What is Energy2D?

Energy2D is an interactive multiphysics simulation tool rooted in computational physics, tailored to model the three main modes of heat transfer: conduction, convection, and radiation, while also incorporating particle dynamics. This software is designed to run smoothly on a variety of computer systems, streamlining the workflow by eliminating the need to switch between different preprocessors, solvers, and postprocessors typically required in computational fluid dynamics studies. Users can conduct "computational experiments" to investigate scientific theories or tackle engineering problems without the necessity for complex mathematical models. Furthermore, ongoing development aims to introduce additional energy transformation types and improve the software's compatibility with various fluid types. Although Energy2D is particularly strong in simulating conduction, its modeling of convection and radiation lacks complete accuracy, indicating that findings related to these processes should be interpreted as qualitative rather than quantitative. More than 40 scientific papers have cited Energy2D as a significant research tool, highlighting its integration into the academic landscape. As the program continues to advance, users can anticipate further enhancements in its features, which could lead to deeper understandings of intricate physical interactions, making it an even more indispensable resource for researchers and engineers alike.

Media

Media

Integrations Supported

Integrations Supported

Raspberry Pi OS

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Free
Free Version

Supported Platforms

Linux

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/modulus

Company Facts

Organization Name

The Concord Consortium

Date Founded

2010

Company Location

United States

Company Website

energy.concord.org/energy2d/

Categories and Features

AI Inference

Not specified

Digital Twin

Not specified

HPC

Not specified

Neural Network

Not specified

Categories and Features

Simulation

Not specified

Thermal Analysis

Not specified

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