Instrumenting Lightweight, Modular Machine Learning Training and Inference in Parallel Solvers

Recent advances in exascale computing have increased the resolution and fidelity of large-scale simulations, while rapid progress in deep learning has accelerated efforts to couple machine learning with physics-based solvers. We present a lightweight, modular in situ coupling methodology that embeds machine learning training and inference directly into simulation workflows using the ParaView and Catalyst APIs. The approach provides C++/Python interoperability via a solver-side data adaptor that packages simulation state into Conduit Nodes and a Catalyst-driven Python “bridge script” that converts solver fields into NumPy/PyTorch representations with minimal intrusion into the solver code. We describe the design and instrumentation required to integrate the framework and demonstrate it within a proxy (mini-app) of the HARVEY vascular flow solver. To illustrate practical usage, we implement both in situ training and in situ inference of a point-cloud autoencoder running concurrently with the solver. We report scalability and overhead characteristics and show that the approach enables distributed online ML workflows without language unification or major solver refactoring.