5 papers
Turbulence Physics Governs a Scaling Law for the Machine-Learning Predictability Ceiling in Chaotic Flow
Jiashun Guan, Haoyang Hu, Yunxiao Ren +2
For centuries, the intrinsic chaos of unsteady fluid motion has stood as a formidable barrier to long-term forecasting. While machine learning (ML) has recently emerged as a transf…
Deep Koopman Iterative Learning and Stability-Guaranteed Control for Unknown Nonlinear Time-Varying Systems
Hengde Zhang, Yunxiao Ren, Zhisheng Duan +2
This paper proposes a Koopman-based framework for modeling, prediction, and control of unknown nonlinear time-varying systems. We present a novel Koopman-based learning method for…
Optimality Deviation using the Koopman Operator
Yicheng Lin, Bingxian Wu, Nan Bai +2
This paper investigates the impact of approximation error in data-driven optimal control problem of nonlinear systems while using the Koopman operator. While the Koopman operator e…
Integrating Uncertainties for Koopman-Based Stabilization
Yicheng Lin, Bingxian Wu, Nan Bai +4
Over the past decades, the Koopman operator has been widely applied in data-driven control, yet its theoretical foundations remain underexplored. This paper establishes a unified f…
Vortex-Induced Drag Forecast for Cylinder in Non-uniform Inflow
Jiashun Guan, Haoyang Hu, Tianfang Hao +3
In this letter, a physics-based data-driven strategy is developed to predict vortex-induced drag on a circular cylinder under non-uniform inflow conditions - a prevalent issue for…