A structure-preserving machine learning framework for accurate prediction of structural dynamics for systems with isolated nonlinearities-Reference-Cited by-同舟云学术

A structure-preserving machine learning framework for accurate prediction of structural dynamics for systems with isolated nonlinearities

Published:2024-05 Issue: Volume:213 Page:111340
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Najera-Flores David A.^ORCID,Quinn D. Dane^ORCID,Garland Anthony,Vlachas Konstantinos^ORCID,Chatzi Eleni^ORCID,Todd Michael D.^ORCID

Funder

U.S. Department of Energy

National Nuclear Security Administration

Sandia National Laboratories

Publisher

Elsevier BV

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1. Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems;ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering;2024-08-17

2. State-space reconstruction from partial observables using an invertible neural network with structure-preserving properties for nonlinear structural dynamics;Nonlinear Dynamics;2024-07-22