Results 11 to 20 of about 3,942,919 (384)
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
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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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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
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Computability and Physical Theories [PDF]
Foundations of Physics, 1986 The familiar theories of physics have the feature that the application of the theory to make predictions in specific circumstances can be done by means of an algorithm. We propose a more precise formulation of this feature --- one based on the issue of whether or not the physically measurable numbers predicted by the theory are computable in the ...
James B. Hartle, Robert Geroch
openaire +3 more sources
Equivariant neural networks for spin dynamics simulations of itinerant magnets
Machine Learning: Science and Technology, 2023 I present a novel equivariant neural network architecture for the large-scale spin dynamics simulation of the Kondo lattice model. This neural network mainly consists of tensor-product-based convolution layers and ensures two equivariances: translations ...
Yu Miyazaki
doaj +1 more source
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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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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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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