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Machine learning for sampling high-dimensional probability distributions in lattice field theory / Shanahan, Phiala (speaker) (Massachusetts Institute of Technology)
As machine learning algorithms continue to enable and accelerate physics calculations, the development of problem-specific physics-informed machine learning approaches is becoming more sophisticated, impactful, and important. I will describe recent advances in generative modelling emerging from the challenge of exact sampling from known probability distributions in the context of lattice quantum field theory calculations in particle and nuclear physics. [...]
2023 - 4227. EP-IT Data Science Seminars External link: Event details In : Machine learning for sampling high-dimensional probability distributions in lattice field theory

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Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning / Shanahan, Phiala (MIT) ; Terao, Kazuhiro (SLAC) ; Whiteson, Daniel (UC, Irvine) ; Aarts, Gert (Swansea U. ; ECT, Trento ; Fond. Bruno Kessler, Trento) ; Adelmann, Andreas (Northeastern U. ; PSI, Villigen) ; Akchurin, N. (Texas Tech.) ; Alexandru, Andrei (George Washington U. ; Maryland U.) ; Amram, Oz (Johns Hopkins U.) ; Andreassen, Anders (Google Inc.) ; Apresyan, Artur (Fermilab) et al.
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. [...]
arXiv:2209.07559 ; FERMILAB-CONF-22-719-ND-PPD-QIS-SCD. -
Fermilab Library Server - eConf - Fulltext - Fulltext

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Vedi anche: autori con nomi simili
66	Shanahan, P
2	Shanahan, P D
19	Shanahan, P E
18	Shanahan, P.
1	Shanahan, P.E.
1	Shanahan, P.N.
1	Shanahan, Patrick
2	Shanahan, Patrick D
11	Shanahan, Peter
2	Shanahan, Phiala E

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