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NVIDIA PhysicsNeMo is an open-source deep learning framework designed for building artificial intelligence models that incorporate physical laws and scientific knowledge into machine learning workflows. The framework focuses on the emerging field of physics-informed machine learning, where neural networks are used alongside physical equations to model complex scientific systems. PhysicsNeMo provides modular Python components that allow developers to create scalable training and inference pipelines for models that combine data-driven learning with physics-based constraints. It is built on top of the PyTorch ecosystem and integrates with GPU-accelerated computing environments to handle computationally demanding simulations and datasets. The framework supports a wide range of scientific applications, including computational fluid dynamics, climate modeling, weather prediction, and engineering simulations.
Features
- Framework for building physics-informed machine learning models
- PyTorch-based architecture optimized for GPU computing
- Reference pipelines for scientific and engineering simulations
- Support for hybrid models combining physics equations and data-driven learning
- Pretrained models and modular components for rapid experimentation
- Scalable training and inference workflows for scientific AI applications
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