Results 151 to 160 of about 8,084,029 (292)
Invariant Physics-Informed Neural Networks for Ordinary Differential Equations [PDF]
Physics-informed neural networks have emerged as a prominent new method for solving differential equations. While conceptually straightforward, they often suffer training difficulties that lead to relatively large discretization errors or the failure to ...
Valiquette, Francis +2 more
core
Point neuron learning: a new physics-informed neural network architecture
EURASIP Journal on Audio, Speech, and Music ProcessingMachine learning and neural networks have advanced numerous research domains, but challenges such as large training data requirements and inconsistent model performance hinder their application in certain scientific problems. To overcome these challenges,
Hanwen Bi, Thushara D. Abhayapala
doaj +1 more source
GeS as an Emerging OTS Material: Atomic Structure and Modeling of ON/OFF Switching
Advanced Functional Materials, EarlyView.The structural puzzle of glassy GeS, an advanced memory‐selector material, has finally been disentangled, revealing a patchwork of mixed tetrahedral and trigonal species. Under a high electric field, over‐ and under‐coordinated Ge and S defects, together with three‐membered rings, form an interconnected network associated with localized and extended ...
Andrey Tverjanovich +13 more
wiley +1 more source
Estimating Soil Organic Carbon from Multispectral Images Using Physics-Informed Neural Networks
Estimating Soil Organic Carbon from Multispectral Images Using Physics-Informed Neural ...
M Paul (7687142) +4 more
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Physics-informed neural network with adaptive activation for power flow
International Journal of Electrical Power & Energy SystemsWe introduce a physics-informed neural network for power flow (PINN4PF) that effectively captures the nonlinear dynamics of large-scale modern power systems.
Zeynab Kaseb +3 more
doaj +1 more source
Advanced Functional Materials, EarlyView.Neuromorphic devices for extreme environments are interpreted through the physical states that store and process information. Radiation, temperature extremes, and vacuum can perturb vacancies, filaments, trapped charges, polarization, interfaces, and readout signals.
Taehyun Park +7 more
wiley +1 more source
Physics-informed neural network solves minimal surfaces in curved spacetime
Machine Learning: Science and TechnologyWe develop a flexible framework based on physics-informed neural networks for solving boundary value problems involving minimal surfaces in curved spacetimes, with a particular emphasis on singularities and moving boundaries.
Koji Hashimoto +4 more
doaj +1 more source
Configuration‐Encoded Cell Alignment by Light‐Gated Silicon Photovoltaic Microcell Arrays
Advanced Functional Materials, EarlyView.Light‐gated silicon photovoltaic microcells convert pulsed illumination into localized electrical cues. Arranging otherwise similar microcells into four‐lobed, six‐lobed, or circular patterns directs fibroblasts into corresponding local arrangements without wired stimulation.
Yifan Tao +7 more
wiley +1 more source
Advanced Functional Materials, EarlyView.Neuromorphic plasticity in OECTs is governed by the post‐stimulus evolution of coupled ionic and electronic states. Semiconductor chemistry and morphology, electrolyte composition and interfaces, and device architecture regulate ion injection, transport, confinement, and release, shifting responses from rapidly relaxing STP to metastable memory and ...
Inho Lee +9 more
wiley +1 more source
<p>This code is for our paper <a href="https://www.nature.com/articles/s41467-024-48779-z"><strong>Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis</strong></a>.</p> <
Fujin Wang
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