Machine Learning, Reservoir Computing, and Nonlinear Dynamics

该项目的目标是对机器学习预测非线性动力系统演化的机制有基本的了解，并探索如何利用这些知识来改进现有的机器学习预测和设计新的能力。实现这一目标将促进我们对一系列潜在的重要应用的理解，这些应用可以用动态系统的数学方法来描述，从基因治疗到天气预报。研究生从事该项目的研究。研究者使用非线性动力学和混沌理论的概念来理解机器学习技术何时，如何以及为什么有效地预测动力系统的状态演变，包括考虑大型和复杂的动力系统（例如，基于空间的系统）。此外，他还考虑了“气候保护”问题，这意味着机器学习工具能够通过测量其状态演化的时间序列来复制一个鲜为人知的动力系统的长期遍历特性。研究者利用这种能力来制定机器学习工具来确定未知系统动力学的遍历特性（例如，李雅普诺夫指数谱）。研究生从事该项目的研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The goal of this project is to obtain basic understanding of the mechanisms by which machine learning can predict the evolution of nonlinear dynamical systems, and to explore how to use this knowledge to improve existing machine learning prediction and devise new capabilities. Achieving this goal would advance our understanding of a host of potential important applications that can be described mathematically by dynamical systems, ranging from gene therapies to weather prediction. Graduate students are engaged in the research of the project.The investigator uses concepts from nonlinear dynamics and chaos theory to obtain understanding of when, how, and why machine learning techniques are effective in predicting the evolution of the state of a dynamical system, including consideration of dynamical systems that are large and complex (e.g., spatially based systems). In addition, he considers the issue of "climate preservation," by which is meant the ability of machine learning tools to replicate the long-term ergodic properties of a dynamical system about which little is known, from measurements of time series of its state evolution. The investigator uses this ability to formulate machine learning tools for determining ergodic properties (e.g., Lyapunov exponent spectra) of the dynamics of the unknown system. Graduate students are engaged in the research of the project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.