复杂系统的多尺度模拟:整合知识与数据的视角

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Huandong Wang, Huan Yan, Can Rong, Yuan Yuan, Fenyu Jiang, Zhenyu Han, Hongjie Sui, Depeng Jin, Yong Li
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引用次数: 0

摘要

复杂系统仿真在理解、预测和控制各种复杂系统方面发挥着不可替代的作用。在过去的几十年中,多尺度仿真技术以其卓越的能力克服了复杂系统仿真所面临的机理未知、计算成本昂贵等难题,引起了越来越多的关注。在本调查中,我们将从知识和数据的角度系统回顾复杂系统多尺度模拟的相关文献。首先,我们将介绍有关模拟复杂系统和复杂系统尺度的背景知识。然后,我们将多尺度建模和仿真的主要目标分为五类,分别考虑尺度明确的情景和尺度不明确的情景。在总结了基于知识和数据线索的多尺度模拟的一般方法后,我们介绍了为实现不同目标所采用的方法。最后,我们介绍了多尺度模拟在典型物质系统和社会系统中的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data

Complex system simulation has been playing an irreplaceable role in understanding, predicting, and controlling diverse complex systems. In the past few decades, the multi-scale simulation technique has drawn increasing attention for its remarkable ability to overcome the challenges of complex system simulation with unknown mechanisms and expensive computational costs. In this survey, we will systematically review the literature on multi-scale simulation of complex systems from the perspective of knowledge and data. Firstly, we will present background knowledge about simulating complex systems and the scales in complex systems. Then, we divide the main objectives of multi-scale modeling and simulation into five categories by considering scenarios with clear scale and scenarios with unclear scale, respectively. After summarizing the general methods for multi-scale simulation based on the clues of knowledge and data, we introduce the adopted methods to achieve different objectives. Finally, we introduce the applications of multi-scale simulation in typical matter systems and social systems.

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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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