Introducing the PhysicsNeMo Diffusion Module: Composable, Extensible Generative Modeling for Physics-AI
In physics-AI, many problems have not a single answer but a distribution of possible ones, which is what diffusion models sample. The PhysicsNeMo diffusion module brings diffusion to scientific data, built on PyTorch and included in the open-source PhysicsNeMo library.
Its composable components range from ready-to-use defaults to fully custom research implementations, serving a CAE engineer, a weather modeler, and a diffusion researcher alike. They cover the whole workflow, from training a model to sampling from it. A single trained model then serves many tasks at inference, drawing large ensembles, solving inverse problems by sampling the posterior, and enforcing physical constraints, without retraining. The module is designed for high throughput on large ensembles and for scaling to the large domains typical of scientific applications. In this post, we'll show what the module unlocks, from large ensembles to data assimilation to physics-constrained generation, and how its abstractions fit together.