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TDOR-MPINNs: Multi-output physics-informed neural networks based on time differential order reduction for solving coupled Klein–Gordon–Zakharov systems: TDOR-MPINNs: multi-output physics-informed neural networks based on time differential order reduction for solving coupled Klein-Gordon-Zakharov systems

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Title: TDOR-MPINNs: Multi-output physics-informed neural networks based on time differential order reduction for solving coupled Klein–Gordon–Zakharov systems: TDOR-MPINNs: multi-output physics-informed neural networks based on time differential order reduction for solving coupled Klein-Gordon-Zakharov systems
Authors: Jiahuan He, Yang Liu, Hong Li
Source: Results in Applied Mathematics, Vol 22, Iss, Pp 100462-(2024)
Publisher Information: Elsevier BV, 2024.
Publication Year: 2024
Subject Terms: Reliability and life testing, multi-output physics-informed neural networks, time differential order reduction, Computational learning theory, Central limit and other weak theorems, Applications of statistics to physics, Time differential order reduction, 01 natural sciences, Multi-output physics-informed neural networks, coupled Klein-Gordon-Zakharov (KGZ) systems, Error bounds for initial value and initial-boundary value problems involving PDEs, Coupled Klein–Gordon–Zakharov (KGZ) systems, 0103 physical sciences, QA1-939, Motion of charged particles, Monte Carlo methods applied to problems in optics and electromagnetic theory, reliability analysis, Waves and radiation in optics and electromagnetic theory, Reliability analysis, Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems, Mathematics, Artificial neural networks and deep learning
Description: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Document Type: Article
File Description: application/xml
Language: English
ISSN: 2590-0374
DOI: 10.1016/j.rinam.2024.100462
Access URL: https://zbmath.org/7878064
https://doi.org/10.1016/j.rinam.2024.100462
https://doaj.org/article/802cd29f5fed4dfbb0241b8a72763372
Rights: CC BY NC
Accession Number: edsair.doi.dedup.....e8576041e1e141afb4cd0e48f455638c
Database: OpenAIRE
Description
ISSN:25900374
DOI:10.1016/j.rinam.2024.100462