Special Issue on Contributions to Machine Learning and Dynamical Systems

摘要截稿:

全文截稿: 2019-09-01

影响因子: 1.96

期刊难度:

CCF分类: 无

中科院JCR分区:

• 大类 : 物理 - 3区

• 小类 : 应用数学 - 2区

• 小类 : 物理：数学物理 - 3区

• 小类 : 物理：综合 - 3区

Overview

Since its inception in the 19th century through the efforts of Poincaré and Lyapunov, the theory of dynamical systems addresses the qualitative behaviour of dynamical systems as understood from models. From this perspective, the modeling of dynamical processes in applications requires a detailed understanding of the processes to be analyzed. This deep understanding leads to a model, which is an approximation of the observed reality and is often expressed by a system of Ordinary/Partial, Underdetermined (Control), Deterministic/Stochastic differential or difference equations. While models are very precise for many processes, for some of the most challenging applications of dynamical systems (such as climate dynamics, brain dynamics, biological systems or the financial markets), the development of such models is notably difficult.

On the other hand, the field of machine learning is concerned with algorithms designed to accomplish a certain task, whose performance improves with the input of more data. Applications for machine learning methods include computer vision, stock market analysis, speech recognition, recommender systems and sentiment analysis in social media. The machine learning approach is invaluable in settings where no explicit model is formulated, but measurement data is available. This is frequently the case in many systems of interest, and the development of data-driven technologies is becoming increasingly important in many applications.

The intersection of the fields of dynamical systems and machine learning is largely unexplored, and the goal of this special issue is to bring together contributions from researchers from these fields to fill the gap between the theories of dynamical systems and machine learning in the following directions:

Machine Learning for Dynamical Systems: how to analyze dynamical systems on the basis of observed data rather than attempt to study them analytically.

Dynamical Systems for Machine Learning: how to analyze algorithms of Machine Learning using tools from the theory of dynamical systems.

There are two possible formats for the papers: research contributions and tutorials.