1 citations · 1 across the 4 of their papers we have counts for
4 papers
Data-driven Nonlinear Modal Analysis with Physics-constrained Deep Learning: Numerical and Experimental Study
Abdolvahhab Rostamijavanani, Shanwu Li, Yongchao Yang
To fully understand, analyze, and determine the behavior of dynamical systems, it is crucial to identify their intrinsic modal coordinates. In nonlinear dynamical systems, this tas…
Data-driven identification of nonlinear dynamical systems with LSTM autoencoders and Normalizing Flows
Abdolvahhab Rostamijavanani, Shanwu Li, Yongchao Yang
While linear systems have been useful in solving problems across different fields, the need for improved performance and efficiency has prompted them to operate in nonlinear modes.…
Data-driven nonlinear modal identification of nonlinear dynamical systems with physics-constrained Normalizing Flows
Abdolvahhab Rostamijavanani, Shanwu Li, Yongchao Yang
Identifying the intrinsic coordinates or modes of the dynamical systems is essential to understand, analyze, and characterize the underlying dynamical behaviors of complex systems.…
Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network
Abdolvahhab Rostamijavanani, Shanwu Li, Yongchao Yang
This work presents a data-driven solution to accurately predict parameterized nonlinear fluid dynamical systems using a dynamics-generator conditional GAN (Dyn-cGAN) as a surrogate…