Physics-informed discretization-independent deep compositional operator network-Reference-Cited by-同舟云学术

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Physics-informed discretization-independent deep compositional operator network

Published:2024-11 Issue: Volume:431 Page:117274
ISSN:0045-7825
Container-title:Computer Methods in Applied Mechanics and Engineering
language:en
Short-container-title:Computer Methods in Applied Mechanics and Engineering

Author:

Zhong Weiheng^ORCID,Meidani Hadi^ORCID

Publisher

Elsevier BV

Reference41 articles.

1. A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials;Goswami;Comput. Methods Appl. Mech. Engrg.,2022

2. Climax: A foundation model for weather and climate;Nguyen,2023

3. Physics-informed learning of governing equations from scarce data;Chen;Nat. Commun.,2021

4. Physics informed neural networks for continuum micromechanics;Henkes;Comput. Methods Appl. Mech. Engrg.,2022

5. A concise guide to modelling the physics of embodied intelligence in soft robotics;Mengaldo;Nat. Rev. Phys.,2022

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