5 papers
Active Diffusion-Based Inference for Ill-Posed Inverse Problems under Incomplete Priors
Jitao Xu, Nobuo Sato, Yaohang Li
Many scientific and engineering applications require estimating unknown parameters from experimentally observable data -- an inverse problem that is inherently challenging due to n…
Learning Transverse Momentum Distributions from Raw Scattering Events via Conditional Diffusion
Jitao Xu, Christopher Cocuzza, Kevin Braga +3
Extracting transverse momentum dependent parton distribution functions (TMD PDFs) from semi-inclusive deep inelastic scattering (SIDIS) data is a central goal of the nucleon struct…
Multi-Dataset Inverse Problem Solving with Distributed Generative AI
Daniel Lersch, Steven Goldenberg, Johann Rudi +5
Extracting a shared set of unknown, not directly measurable quantities from multiple, heterogeneous datasets is a common challenge across scientific domains. A prominent example is…
Toward an event-level analysis of hadron structure using differential programming
Kevin Braga, Markus Diefenthaler, Steven Goldenberg +7
Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon degrees of freedom is a central goal in nuclear and particle physics. This effort lies at…
SAGIPS: A Scalable Asynchronous Generative Inverse Problem Solver
Daniel Lersch, Malachi Schram, Zhenyu Dai +4
Large scale, inverse problem solving deep learning algorithms have become an essential part of modern research and industrial applications. The complexity of the underlying inverse…