Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Almaeen, Manal"'
Autor:
Almaeen, Manal, Alghamdi, Tareq, Kriesten, Brandon, Adams, Douglas, Li, Yaohang, Lin, Huey-Wen, Liuti, Simonetta
We develop a new methodology for extracting Compton form factors (CFFs) in from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM).
Externí odkaz:
http://arxiv.org/abs/2405.05826
Autor:
Almaeen, Manal, Grigsby, Jake, Hoskins, Joshua, Kriesten, Brandon, Li, Yaohang, Lin, Huey-Wen, Liuti, Simonetta
We develop a framework to establish benchmarks for machine learning and deep neural networks analyses of exclusive scattering cross sections (FemtoNet). Within this framework we present an extraction of Compton form factors for deeply virtual Compton
Externí odkaz:
http://arxiv.org/abs/2207.10766
Autor:
Almaeen, Manal, Grigsby, Jake, Hoskins, Joshua, Kriesten, Brandon, Li, Yaohang, Lin, Huey-Wen, Liuti, Simonetta, Maichum, Sorawich
We present a physics informed deep learning technique for Deeply Virtual Compton Scattering (DVCS) cross sections from an unpolarized proton target using both an unpolarized and polarized electron beam. Training a deep learning model typically requir
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cf61d46f24e1acf7b69eb4cbbf80132c
In many scientific applications, Inverse problems are challenging. An inverse problem is the process of inferring unknown parameters from observable ones. In this poster, we present our prototype using Point Cloud-based Variational Autoencoder mappin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea8cb17ad8fc83842c8e86005273b377
Autor:
Almaeen, Manal
In this work, we propose the Variational Autoencoder Inverse Mapper (VAIM) to solve inverse problems, where there is a demand to accurately restore hidden parameters from indirect observations. VAIM is an autoencoder-based neural network architecture
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97e672f5c50def4c1304b9f3a1ce6156