Data-Driven Modeling of Parameterized Nonlinear Dynamical Systems with a Dynamics-Embedded Conditional Generative Adversarial Network-Reference-Cited by-同舟云学术

Data-Driven Modeling of Parameterized Nonlinear Dynamical Systems with a Dynamics-Embedded Conditional Generative Adversarial Network

Published:2023-11 Issue:11 Volume:149 Page:
ISSN:0733-9399
Container-title:Journal of Engineering Mechanics
language:en
Short-container-title:J. Eng. Mech.

Author:

Rostamijavanani A.¹^ORCID,Li Shanwu²^ORCID,Yang Yongchao³^ORCID

Affiliation:

1. Ph.D. Candidate, Dept. of Mechanical Engineering and Engineering Mechanics, Michigan Technological Univ., Houghton, MI 49931. ORCID: .

2. Postdoctoral Researcher, Dept. of Mechanical Engineering and Engineering Mechanics, Michigan Technological Univ., Houghton, MI 49931. ORCID: .

3. Assistant Professor, Dept. of Mechanical Engineering and Engineering Mechanics, Michigan Technological Univ., Houghton, MI 49931 (corresponding author). ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Subject

Mechanical Engineering,Mechanics of Materials

Link

https://ascelibrary.org/doi/pdf/10.1061/JENMDT.EMENG-7038

Reference34 articles.

1. Identification of time-varying cable tension forces based on adaptive sparse time-frequency analysis of cable vibrations

2. Brunner W. 1961. “An iteration procedure for parametric model building and boundary value problems.” In Proc. Papers Presented at the May 9–11 1961 Western Joint IRE-AIEE-ACM Computer Conf. 519–533. New York: Association for Computing Machinery.

3. Discovering governing equations from data by sparse identification of nonlinear dynamical systems

4. An Online Data-Driven Method to Locate Forced Oscillation Sources From Power Plants Based on Sparse Identification of Nonlinear Dynamics (SINDy)

5. Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network