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The past decade has seen a rapid increase in the development and use of Python-based open-source libraries for data-driven methods and machine learning. The widespread adoption of these libraries across scientific and engineering disciplines highlights their growing prominence in accelerating simulations. This talk presents an in-situ (in-memory/online) workflow, which requires minimal modifications, designed for mature AMReX codes to leverage the rich Python-ecosystem. The workflow enables language-interoperable data transfer through runtime coupling of AMReX and pyAMReX via Multiple Program Multiple Data (MPMD) mode of Message Passing Interface (MPI). This capability is demonstrated through multiscale modeling of granular flows, which involves coupling low-fidelity continuum and high-fidelity particle-based methods. The computational intractability of straightforward coupling between low- and high-fidelity methods is addressed using adaptively (on-the-fly) evolving neural network ensembles, implemented in PyTorch Distributed Data Parallel, as a surrogate for the expensive high-fidelity solver. The scalability of the current approach across multiple GPUs will also be discussed.
