Nvidia Modulus Symbolic (Modulus Sym)

Nvidia Modulus

NVIDIA Modulus blends physics, as expressed by governing partial differential equations (PDEs), boundary conditions, and training data to build high-fidelity, parameterized, surrogate deep learning models. The platform abstracts the complexity of setting up a scalable training pipeline, so you can leverage your domain expertise to map problems to an AI model’s training and develop better neural network architectures. Available reference application serve as a great starting point for applying the same principles to new use cases.

Whether you’re a researcher looking to develop novel AI-based approaches for reimagining engineering and scientific simulations or you’re an engineer looking to accelerate design optimization and digital twin applications, the Modulus platform can support your model development. Modulus offers a variety of approaches for training physics-based neural network models, from purely physics-driven models with physics-informed neural networks (PINNs) to physics-based, data-driven architectures such as neural operators.

Modulus has been rearchitected into modules:

Modulus Core is the base module that consists of the core components of the framework for developing Physics-ML models

Modulus Sym provides an abstraction layer for using PDE-based symbolic loss functions

Modulus Launch provides optimized training recipes for data driven Physics-ML models

In this article, we will take Modulus Sym as example.