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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
Esteban P. Samaniego, Cosmin Anitescu, Somdatta Goswami, Vien Minh Nguyen‐Thanh, Hongwei Guo, Khader M. Hamdia, Xiaoying Zhuang, Timon Rabczuk
University of Cuenca Bauhaus-Universität Weimar Leibniz University Hannover Ton Duc Thang University
来源Computer Methods in Applied Mechanics and Engineering
年份2020
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物理Model Reduction and Neural Networks
Numerical methods in engineering · Neural Networks and Applications
参考文献 54
Automated Solution of Differential Equations by the Finite Element Method: The FEniCS Book
被引 1,483Anders Logg, Kent‐André Mardal, Garth N. Wells · CERN Document Server (European Organization for Nuclear Research) · 2012
THEORY OF PLATES AND SHELLS
被引 12,018Stephen P. Timoshenko, S. Woinowsky-Krieger · UCLA Biblioteca de Ingenieria Civil · 1959
Understanding the difficulty of training deep feedforward neural networks
被引 12,459Xavier Glorot, Yoshua Bengio · 2010
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引用本文 2,110
Deep learning modelling techniques: current progress, applications, advantages, and challenges
被引 1,071Shams Forruque Ahmed, Md. Sakib Bin Alam, Maruf Hassan · Artificial Intelligence Review · 2023
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
被引 906Laith Alzubaidi, Jinshuai Bai, Aiman Al-Sabaawi · Journal Of Big Data · 2023
hp-VPINNs: Variational physics-informed neural networks with domain decomposition
被引 836Ehsan Kharazmi, Zhongqiang Zhang, George Em Karniadakis · Computer Methods in Applied Mechanics and Engineering · 2020
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