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arXiv Machine Learning By Daniel Lersch, Steven Goldenberg, Johann Rudi, Markus Diefenthaler, Kevin Brager, Xingfu Wu, Yaohang Li, Nobuo Sato

Multi-Dataset Inverse Problem Solving with Distributed Generative AI

Read the original on arXiv Machine Learning →

The paper introduces a generalized framework for solving inverse problems across multiple heterogeneous datasets using distributed generative AI. It extends the SAGIPS framework to handle non‑identically distributed data, employing separate forward operators and discriminators for each dataset while guiding a shared generator toward global parameter consistency. Validation on a multi‑detector scattering experiment demonstrates robustness to varying data fidelities and scalability on multi‑GPU systems.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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