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July 2026

A Probabilistic Surrogate for Kinetic Monte Carlo Simulations

Kinetic Monte Carlo (KMC) is a foundational method for simulating systems that evolve through a sequence of discrete, random events. It appears across a wide range of science and engineering simulations: defect and trap generation in semiconductor devices (device reliability and endurance simulations in TCAD), ion transport and degradation in battery materials, catalytic reactions on surfaces, crystal and thin-film growth, and microstructure evolution in metals and alloys. KMC is a highly effective method, but it is computationally expensive, and it has a few characteristics that set it apart from the conventional time-stepping and integration-based solvers on which most deep-learning surrogates are built. The time between events is a random quantity produced by the solver, and the solver is itself stochastic, so the same starting configuration yields a different trajectory on every run.

The new kinetic Monte Carlo example in PhysicsNeMo is an end-to-end recipe, from training to trajectory generation, for a probabilistic surrogate that emulates a KMC solver one event at a time. From the current state, it predicts a full probability distribution over the next event, and running it forward repeatedly turns a single starting configuration into an ensemble of plausible trajectories with calibrated uncertainty.

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.