Tag: physics.comp-ph
arXiv:2602.21361v1 Announce Type: cross Abstract: Ptychographic imaging at synchrotron and XFEL sources requires dense overlapping scans, limiting throughput and increasing dose. Extending coherent diffractive imaging …
arXiv:2602.21466v1 Announce Type: new Abstract: $E(3)$-equivariant neural networks have proven to be effective in a wide range of 3D modeling tasks. A fundamental operation of …
arXiv:2602.15890v1 Announce Type: cross Abstract: This work introduces a neural operator based surrogate modeling framework for neutron transport computation. Two architectures, the Deep Operator Network …
arXiv:2602.17089v1 Announce Type: new Abstract: Diffusion models recently developed for generative AI tasks can produce high-quality samples while still maintaining diversity among samples to promote …
arXiv:2602.16213v1 Announce Type: new Abstract: This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure …
arXiv:2602.13805v1 Announce Type: new Abstract: Untrained neural networks (UNNs) offer high-fidelity electromagnetic inverse scattering reconstruction but are computationally limited by high-dimensional spatial-domain optimization. We propose …
