Results 21 to 30 of about 4,251,069 (374)
Parameterized neural ordinary differential equations: applications to computational physics problems [PDF]
Proceedings of the Royal Society A, 2020 This work proposes an extension of neural ordinary differential equations (NODEs) by introducing an additional set of ODE input parameters to NODEs. This extension allows NODEs to learn multiple dynamics specified by the input parameter instances.
Kookjin Lee, E. Parish
semanticscholar +1 more source
Search for high-Tcconventional superconductivity at megabar pressures in the lithium-sulfur system [PDF]
Physical review B, 2016 Superconductivity at high-pressures has attracted considerable interest after the report of a record critical temperature (${T}_{c}$) of 203 K in sulfur hydride (H${}_{3}$S) at 200 GPa.
C. Kokail +5 more
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Educational Administration: Theory and Practice, 2022 This study aimed to analyze the factor model affecting the implementation of the Generative Learning Model with a Cognitive Conflict Strategy in the Computational Physics Course during the COVID-19 pandemic era.
A. Akmam +4 more
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Journal of Physics: Conference Series, 2022 The discovery learning model with the strategy of exploring questions no longer increases students’ understanding of Computational Physics, for this reason, a cognitive conflict-based generative learning model is developed.
A. Akmam +4 more
semanticscholar +1 more source
A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge [PDF]
Scientific Reports, 2022 Discovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields. We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED), which ...
Liron Simon Keren +2 more
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Jurnal Neutrino: Jurnal Fisika dan Aplikasinya, 2021 Neutrino can carry information from places that cannot be reached by the usual detection mechanism because it has a very weak interaction with matter. This can be utilized to study the heat flow process inside the earth by using information carried by ...
Akmal Ferdiyan +1 more
doaj +1 more source
An introduction to programming Physics-Informed Neural Network-based computational solid mechanics [PDF]
International Journal of Computational Methods, 2022 Physics-informed neural network (PINN) has recently gained increasing interest in computational mechanics. In this work, we present a detailed introduction to programming PINN-based computational solid mechanics.
Jinshuai Bai +8 more
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Turbulence in Accretion Disks: Vorticity Generation and Angular Momentum Transport via the Global Baroclinic Instability [PDF]
, 2002 In this paper we present the global baroclinic instability as a source for vigorous turbulence leading to angular momentum transport in Keplerian accretion disks.
H. Klahr +5 more
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Six textbook mistakes in computational physics [PDF]
, 2020 This article discusses several erroneous claims which appear in textbooks on numerical methods and computational physics. These are not typos or mistakes an individual author has made, but widespread misconceptions.
A. Gezerlis, M. Williams
semanticscholar +1 more source
Transverse confinement of electron beams in a 2D optical lattice for compact coherent x-ray sources
New Journal of Physics, 2021 Compact coherent x-ray sources have been the focus of extensive research efforts over the past decades. As a result, several novel schemes like optical and nano-undulators for generating x-ray emissions in ‘table-top’ setups are proposed, developed, and ...
Arya Fallahi +2 more
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