Welcome to the NVIDIA PhysicsNeMo blog! Here you'll find the latest insights, tutorials, and updates about the PhysicsNeMo framework. Discover how neural operators, GNNs, and generative AI models can enhance your engineering simulations and workflows.
Transolver, a highlight at ICML 2024 and a leading CFD surrogate model, is now significantly enhanced within PhysicsNeMo. Originally integrated by co-author Huakun Luo, these updates empower AI-Physics developers to rapidly prototype, scale, and apply surrogate models to enterprise applications like automotive aerodynamics.
This tutorial blog is designed for AI researchers and scientific machine learning (SciML) developers who wish to leverage PhysicsNeMo in creating AI Physics models. NVIDIA PhysicsNeMo is built upon PyTorch, the leading standard for AI model development. Organizations can utilize the native PyTorch stack and PhysicsNeMo to go from proof-of-concept development to production-scale training and inference workflows, achieving significant performance and operational efficiencies through enterprise-hardened PhysicsNeMo modules. This blog will demonstrate how AI researchers and SciML developers can progressively integrate PhysicsNeMo modules into their PyTorch stack.
We're excited to announce the latest PhysicsNeMo release! It's packed with powerful new workflows and recipes for CAE application developers. With a key emphasis on the developer experience, this update is designed to help you build, train, and deploy state-of-the-art Physics AI solutions with unprecedented speed and simplicity. We've streamlined every step of the process, from data prep to model evaluation, so you can focus on innovation, not infrastructure. Here are some key highlights of this release: