Lersch, Daniel https://orcid.org/0000-0002-0356-0754
Schram, Malachi https://orcid.org/0000-0002-3475-2871
Dai, Zhenyu https://orcid.org/0000-0001-6135-7749
Rajput, Kishansingh
Sato, Nobuo https://orcid.org/0000-0002-1535-6208
Wu, Xingfu https://orcid.org/0000-0001-8150-5171
Childers, J Taylor
Goldenberg, Steven https://orcid.org/0000-0002-5264-6298
Funding for this research was provided by:
Nuclear Physics (DE-AC05-06OR23177)
Advanced Scientific Computing Research (DE-AC02-06CH11357)
Article Title: SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver
Journal Title: Machine Learning: Science and Technology
Article Type: paper
Copyright Information: © 2025 The Author(s). Published by IOP Publishing Ltd. Contribution of National Oceanic and Atmospheric Administration is not subject to copyright in the USA
Publication dates
Date Received: 2024-11-01
Date Accepted: 2025-04-03
Online publication date: 2025-04-16