SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver

SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver

Solving large-scale inverse problems using deep-learning algorithms have become an essential part of modern research and industrial applications. The complexity of the underlying inverse problem may require the utilization of high performance computing systems which poses a challenge on the algorith...

Full description

Saved in:
Bibliographic Details
Main Authors: Daniel Lersch, Malachi Schram, Zhenyu Dai, Kishansingh Rajput, Nobuo Sato, Xingfu Wu, J Taylor Childers, Steven Goldenberg
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
generative AI
scalable asynchronous workflows
inverse problem solving
high performance computing
deep learning
Online Access:https://doi.org/10.1088/2632-2153/adc8fb
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doi.org/10.1088/2632-2153/adc8fb

Similar Items