Fast Simulation of Multicomponent Dynamic Systems

TL;DR

This paper presents modeling concepts and algorithmic techniques for creating fast simulations of multicomponent dynamic systems across various fields, emphasizing efficiency and applicability.

Contribution

It introduces specific algorithmic and modeling methods, such as event-driven processing and parallel techniques, tailored for rapid simulation of complex systems.

Findings

Techniques like event-driven processing improve simulation speed.

Parallel processing methods enhance efficiency in large-scale models.

Applications span economics, material science, and communications.

Abstract

A computer simulation has to be fast to be helpful, if it is employed to study the behavior of a multicomponent dynamic system. This paper discusses modeling concepts and algorithmic techniques useful for creating such fast simulations. Concrete examples of simulations that range from econometric modeling to communications to material science are used to illustrate these techniques and concepts. The algorithmic and modeling methods discussed include event-driven processing, ``anticipating'' data structures, and ``lazy'' evaluation, Poisson dispenser, parallel processing by cautious advancements and by synchronous relaxations. The paper gives examples of how these techniques and models are employed in assessing efficiency of capacity management methods in wireless and wired networks, in studies of magnetization, crystalline structure, and sediment formation in material science, in studies of competition in economics.