A randomized neural network based Petrov–Galerkin method for approximating the solution of fractional order boundary value problems-Reference-Cited by-同舟云学术

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A randomized neural network based Petrov–Galerkin method for approximating the solution of fractional order boundary value problems

Published:2024-08 Issue: Volume:23 Page:100493
ISSN:2590-0374
Container-title:Results in Applied Mathematics
language:en
Short-container-title:Results in Applied Mathematics

Author:

Roop John P.

Publisher

Elsevier BV

Reference27 articles.

1. DGM: A deep learning algorithm for solving partial differential equations;Sirignano;J Comput Phys,2018

2. Physics-informed neural networks (PINNs) for fluid mechanics: A review;Cai;Acta Mech Sin,2021

3. Stacked networks improve physics-informed training: applications to neural networks and deep operator networks;Howard;Found Data Sci,2024

4. fPINNs: Fractional physics-informed neural networks;Pang;SIAM J Sci Comput,2019

5. TDOR-MPINNs: Multi-output physics-informed neural networks based on time differential order reduction for solving coupled Klein–Gordon–Zakharov systems;He;Results Appl Math,2024

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