Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks-Reference-Cited by-同舟云学术

Machine Learning Approach to Model Order Reduction of Nonlinear Systems via Autoencoder and LSTM Networks

Published:2021-10 Issue:10 Volume:147 Page:04021061
ISSN:0733-9399
Container-title:Journal of Engineering Mechanics
language:en
Short-container-title:J. Eng. Mech.

Author:

Simpson Thomas¹,Dervilis Nikolaos²,Chatzi Eleni³^ORCID

Affiliation:

1. Institute of Structural Engineering, Dept. of Civil, Environmental and Geomatic Engineering, Eidgenössische Technische Hochschule (ETH) Zürich, Stefano-Franscini Platz 5, Zürich 8093, Switzerland (corresponding author).

2. Senior Lecturer, Dynamics Research Group, Dept. of Mechanical Engineering, Univ. of Sheffield, Sheffield S1 3JD, UK.

3. Professor, Institute of Structural Engineering, Dept. of Civil, Environmental and Geomatic Engineering, Eidgenössische Technische Hochschule (ETH) Zürich, Stefano-Franscini Platz 5, Zürich 8093, Switzerland. ORCID:

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Mechanics of Materials

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29EM.1943-7889.0001971

Reference65 articles.

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5. Learning long-term dependencies with gradient descent is difficult

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