Physicsnemo

When it fits

Use PhysicsNeMo when a project needs a PyTorch-based framework and reusable training recipes for physics or scientific machine learning, especially when an example domain in the repository is relevant.

Who it is for

• Developers building physics-informed or scientific machine-learning models.
• Scientific and engineering teams creating training pipelines or simulation surrogate models.
• Practitioners evaluating model recipes for domains such as aerodynamics, weather forecasting, structural mechanics and geophysics.

Documented uses

• Build and train physics AI models using reusable framework components.
• Create repeatable training pipelines for scientific and engineering machine-learning work.
• Fine-tune deep-learning models for physics applications.
• Explore example recipes for engineering and scientific prediction tasks.