物理/数学 | SCI期刊专刊信息2条

数学

Physica D: Nonlinear Phenomena

Special Issue on Machine Learning and Dynamical Systems

全文截稿: 2019-09-01

影响因子: 1.96

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

网址: https://www.journals.elsevier.com/physica-d-nonlinear-phenomena

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.

数学

Physica D: Nonlinear Phenomena

Special Issue on Discrete Models of Complex Systems: recent trends and analytical challenges

全文截稿: 2020-01-31

影响因子: 1.96

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

网址: https://www.journals.elsevier.com/physica-d-nonlinear-phenomena

Complex systems are ubiquitous. Examples include financial markets and human economies, highway transportation and telecommunication networks, musical improvisation, social networks, and biological systems as development and morphogenesis, the immune system, cancer, and ecology. The key feature of any complex system is that it is composed of a discrete number of interacting entities exhibiting new emerging properties on a larger scale compared to the properties and behaviors of its individual entities at the smaller scale.

Complex systems are studied in the social sciences, engineering, music, physics, biology, and mathematics. The integral part of these interdisciplinary studies forms discrete modelling in terms of cellular automata, lattice-gas cellular automata, agent-based models, or complex networks. These models can be seen as simple digital laboratories to study phenomena exhibited by complex systems like self-organization, pattern formation, cooperation, adaptation, competition, or multi-scale phenomena.

The aim of this special issue is to collect innovative mathematical models and simulation tools which push forward our understanding of the organization principles of complex systems and analysis of their dynamics. Papers studying complex phenomena by cellular automata, artificial neural networks or in multi-agent systems, both theoretical and applied, are especially welcome. A focus on discrete modeling methodologies and their applications to different scales of complex systems dynamics will be favoured.

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