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June 2026

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.

Don’t Yet Trust the Model, Test the Physics

AI physics models are advancing quickly and are beginning to prove their value in enterprise engineering workflows. But a critical bottleneck remains: rigorous, repeatable evaluation. Comparing a new model against the current state of the art still too often means stitching together datasets, metrics, scripts, and baselines by hand. That keeps evaluation behind a skill curtain and slows down both model development and domain expert adoption. To push the state of the art forward at the speed of light, we need to make evaluation easier for the people who understand the physics, the data, and the edge cases best. Their feedback will enable the AI researchers to surgically operate and build new bleeding edge models. This blog highlights the new and improved PhysicsNeMo CFD module to address the current gaps and strengthen this loop between model developers and model evaluators.

Introducing PhysicsNeMo-Mesh: GPU-Accelerated Mesh Processing for Scientific ML

PhysicsNeMo-Mesh is a GPU-accelerated mesh processing module, built on PyTorch and TensorDict, and included in the open-source PhysicsNeMo library. It provides a) a GPU-native, high-performance mesh data structure with design choices that are particularly well-suited for ML workflows, and b) a diverse suite of accelerated mesh operations that can be used to bridge existing gaps between data preprocessing and training/inference.

Its native data format allows loading meshes from disk much more quickly than VTU (9x-88x faster in our testing; hardware-dependent) while preserving the full mesh structure that flat tensor formats like zarr typically discard. This gives you the speed you need for training while retaining the geometric and physical context needed for model- and dataset-agnostic workflows. In this post, we'll show what PhysicsNeMo-Mesh can do for your training pipeline.

Training AI Physics Models for Transient Structural Mechanics Simulations such as Automotive Crash

Simulating automotive crash is one of the most complex computational workloads in computer-aided engineering (CAE). Crash simulation is a highly non-linear, dynamic event that happens in milliseconds. Therefore automotive crash simulations face significant bottlenecks in both human-driven workflows and raw computational demands. These challenges stem from the need to model highly non-linear, split-second events with extreme accuracy to meet strict regulatory standards (like NHTSA or Euro NCAP).