Modulus User Guide

NVIDIA Modulus is a neural network framework that blends the power of physics and partial differential equations (PDEs) with AI to build more robust models for better analysis.

There is a plethora of ways in which ML/NN models can be applied for physics-based systems. These can depend based on the availability of observational data and the extent of understanding of underlying physics. Based on these aspects, the ML/NN based methodologies can be broadly classified into forward (physics-driven), data-driven and hybrid approaches that involve both the physics and data assimilation.

AI in computational sciences

With NVIDIA Modulus, we aim to provide researchers and industry specialists, various tools that will help accelerate your development of such models for the scientific discipline of your need. Experienced users can start with exploring the Modulus APIs and building the models while beginners can use this User Guide as a portal to explore the possibilities of AI in the domain of scientific computation. The User Guide comes in with several examples that will help you jumpstart your development of AI driven models.

For Beginners

New to Modulus? No worries, we will get you up and running with physics driven AI in a few minutes. Check out the following tutorials to get started.