Physics-informed neural networks for data-driven simulation: Advantages, limitations, and opportunities-Reference-Cited by-同舟云学术

Physics-informed neural networks for data-driven simulation: Advantages, limitations, and opportunities

Published:2023-01 Issue: Volume:610 Page:128415
ISSN:0378-4371
Container-title:Physica A: Statistical Mechanics and its Applications
language:en
Short-container-title:Physica A: Statistical Mechanics and its Applications

Author:

Fernández de la Mata Félix,Gijón Alfonso,Molina-Solana Miguel^ORCID,Gómez-Romero Juan

Funder

Spain Ministry of Science and Innovation

Government of Andalusia Ministry of Economy Science Innovation and Employment

Federación Española de Enfermedades Raras

Ministerio de Ciencia e Innovación

Ministry of Economic Affairs and Digital Transformation

Junta de Andalucía

Publisher

Elsevier BV

Subject

Statistical and Nonlinear Physics,Statistics and Probability

Reference32 articles.

1. Finite difference methods for ordinary and partial differential equations: steady-state and time-dependent problems;Randall;Choice Rev. Online,2008

2. Finite Difference Schemes and Partial Differential Equations;Strikwerda,2004

3. Finite Element Analysis with Error Estimators: An Introduction to the FEM and Adaptive Error Analysis for Engineering Students;Akin,2005

4. Finite volume methods;Eymard,2000

5. Deep learning;LeCun;Nature,2015

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