非线性系统
过程(计算)
动态模态分解
模式(计算机接口)
控制理论(社会学)
分解
高斯过程
数学
应用数学
计算机科学
高斯分布
控制(管理)
人工智能
物理
机器学习
化学
有机化学
量子力学
操作系统
作者
Alexandros Tsolovikos,Efstathios Bakolas,David Goldstein
出处
期刊:Journal of Dynamic Systems Measurement and Control-transactions of The Asme
[ASME International]
日期:2024-05-24
卷期号:146 (6)
摘要
Abstract In this work, we consider the problem of learning a reduced-order model of a high-dimensional stochastic nonlinear system with control inputs from noisy data. In particular, we develop a hybrid parametric/nonparametric model that learns the “average” linear dynamics in the data using dynamic mode decomposition with control (DMDc) and the nonlinearities and model uncertainties using Gaussian process (GP) regression and compare it with total least-squares dynamic mode decomposition (tlsDMD), extended here to systems with control inputs (tlsDMDc). The proposed approach is also compared with existing methods, such as DMDc-only and GP-only models, in two tasks: controlling the stochastic nonlinear Stuart–Landau equation and predicting the flowfield induced by a jet-like body force field in a turbulent boundary layer using data from large-scale numerical simulations.
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