Data from: Lightweight, Modular Online Model Inference & Training in Parallel Solvers With Catalyst

Recent advances in computational solvers, combined with the introduction of exascale systems, have greatly increased the resolution and fidelity of large-scale simulations. In parallel, rapid progress and the adoption of deep learning have spurred the development of frameworks that integrate machine learning with scientific computing. We introduce a lightweight, modular in situ coupling framework designed to leverage the Catalyst API to embed machine learning routines directly into simulation workflows. Our approach enables seamless interoperability between C++ and Python applications through the use of a solver-side data adaptor and a ParaView-based Python interpreter. We illustrate the framework's design and usability by instrumenting it within a proxy application of the HARVEY vascular flow solver. To demonstrate its practical utility, we perform both training and inference of a point-cloud autoencoder entirely at runtime.