Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Data

TL;DR

This paper reviews multi-scale simulation techniques for complex systems, emphasizing the integration of knowledge and data to address challenges like unknown mechanisms and high computational costs.

Contribution

It systematically categorizes objectives and methods of multi-scale simulation, highlighting the integration of knowledge and data for complex system modeling.

Findings

Classifies multi-scale simulation objectives into five categories.

Summarizes general methods based on knowledge and data clues.

Discusses applications in matter and social systems.

Abstract

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 system simulation 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.