Results 11 to 20 of about 2,276,450 (359)
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next [PDF]
Journal of Scientific Computing, 2022 Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself.
S. Cuomo +5 more
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A comprehensive guide to the physics and usage of PYTHIA 8.3 [PDF]
SciPost Physics Codebases, 2022 This manual describes the Pythia event generator, the most recent version of an evolving physics tool used to answer fundamental questions in particle physics. The program is most often used to generate high-energy-physics collision “events”, i.e.
C. Bierlich +13 more
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Physics-informed neural networks (PINNs) for fluid mechanics: a review [PDF]
Acta Mechanica Sinica, 2021 Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is ...
Shengze Cai +4 more
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PYTHIA 6.4 Physics and Manual [PDF]
, 2006 The Pythia program can be used to generate high-energy-physics ''events'', i.e. sets of outgoing particles produced in the interactions between two incoming particles.
T. Sjostrand, S. Mrenna, P. Skands
semanticscholar +1 more source
International Journal of Modern Physics A, 2007 Misha Shifman knows much more about conformal symmetry than I do, but I have been thinking hard about it, and I thought it would be fun to present some of my ideas as a little birthday present.
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Physicalism or Anti-physicalism: A Disjunctive Account
Erkenntnis, 2022 AbstractIn this paper, we make a case for the disjunctive view of phenomenal consciousness: consciousness is essentially disjunctive in being either physical or non-physical in the sense that it has both physical and non-physical possible instances.
Baysan, E, Wildman, N
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Reinforcement Learning and Physics
Applied Sciences, 2021 Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the ...
José D. Martín-Guerrero, Lucas Lamata
doaj +1 more source
EDULEARN Proceedings, 2019 Halfway between the experiment and the focus group, between the quiz and a game, we have experienced a new format to \"focus\" on sustainability and the fundamental laws of thermodynamics and its principles. Concepts as reversibility, efficiency and entropy, are then \"visualized\" by the participants, showing the relations with the economic value ...
Domenici, D., Mazzitelli, G.
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Advances in Physics, 2020 A review is given on the foundations and applications of non-Hermitian classical and quantum physics. First, key theorems and central concepts in non-Hermitian linear algebra, including Jordan normal form, biorthogonality, exceptional points, pseudo ...
Yuto Ashida, Z. Gong, Masahito Ueda
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Lepton and Photon Interactions at High Energies, 1992 16 pages, 8 figures. Invited talk at the XIX International Symposium on Lepton and Photon Interactions at High Energies (Stanford, August 1999)
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