Identifying optimal architectures of physics-informed neural networks by evolutionary strategy-Reference-Cited by-同舟云学术

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Identifying optimal architectures of physics-informed neural networks by evolutionary strategy

Published:2023-10 Issue: Volume:146 Page:110646
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Kaplarević-Mališić Ana^ORCID,Andrijević Branka,Bojović Filip,Nikolić Srđan,Krstić Lazar,Stojanović Boban,Ivanović Miloš

Funder

European Commission

European Institute of Innovation and Technology

EIT RawMaterials

Publisher

Elsevier BV

Subject

Software

Reference39 articles.

1. Physics informed deep learning (part I): data-driven solutions of nonlinear partial differential equations;Raissi,2017

2. The old and the new: Can physics-informed deep-learning replace traditional linear solvers?;Markidis;Front. big data,2021

3. Random search for hyper-parameter optimization;Bergstra;J. Mach. Learn. Res.,2012

4. Meta-learning evolutionary artificial neural networks;Abraham,2004

5. A comprehensive survey of neural architecture search: Challenges and solutions;Ren;ACM Comput. Surv.,2021

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of stratified ground consolidation via a physics‐informed neural network utilizing short‐term excess pore water pressure monitoring data;Computer-Aided Civil and Infrastructure Engineering;2024-08-17

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