Machine learning based digital twin for dynamical systems with multiple time-scalesDigital twin technology has a huge potential for widespread applications in different industrial sectors such as infrastructure, aerospace, and automotive. However, practical adoptions of this technology have been slower, ...
《Data-Driven Science and Engineering:Machine Learning, Dynamical Systems, and Control》,作者是华盛顿大学的Steven L. Brunton和J. Nathan Kutz, 全书共分为4个Part:降维与变换、机器学习和数据分析、动力学和控制、降阶模型,如果有需要pdf版本的同学可以私信我 最常见的优化策略 Least-Squares 最小二乘使给定...
Learn Koopman and Transfer operators for Dynamical Systems and Stochastic Processes kooplearn is a Python library designed for learning Koopman or Transfer operators associated with dynamical systems. Given a nonlinear dynamical system xt+1=S(xt), the Koopman operator provides a global linearization of...
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" 链接: https://www.cambridge.org/us/academic/subjects/mathematics/computational-science/data-driven-sc…
Nonlinear dynamical systems, which include models of the Earth's climate, financial markets and complex ecosystems, often undergo abrupt transitions that lead to radically different behavior. The ability to predict such qualitative and potentially disruptive changes is an important problem with far-reachin...
PF-DMD: physics-fusion dynamic mode decomposition for accurate and robust forecasting of dynamical systems with imperfect data and physics. Preprint at https://arxiv.org/abs/2311.15604 (2023). Regazzoni, F., Pagani, S., Salvador, M., Dede’, L. & Quarteroni, A. Learning the intrinsic ...
19-数据驱动科学与工程机器学习、动力系统和控制-Data-Driven Science and Engineering Machine Learning, Dynamical Systems, and Control,机器学习和数据科学,Science,engine,人人文库,
Dimensionality Hyper-Reduction and Machine Learning for Dynamical Systems with Varying Parameters Syuzanna Sargsyan Chair of the Supervisory Committee: Professor J. Nathan Kutz Applied Mathematics This work demonstrates methods for hyper... S Sargsyan 被引量: 1发表: 2016年 加载更多来源...
LINEAR DYNAMICAL SYSTEMS: A MACHINE LEARNING FRAMEWORK FOR FINANCIAL TIME SERIES ANALYSIS KEMBEY GBARAYOR JR Advisor: Professor Amy Greenwald Department of Computer Science, Brown University, Providence, RI, USA Introduction Linear dynamical systems are a class of probabilistic models capable of capturing...
Security-aware analytical framework : A mathematical model and machine learning for dynamical system control in secure environments This study presents a Security-Aware Analytical Framework (SAAF) that is meant to make dynamic system control better in safe places. The framework uses a n... Jaya Cha...