A toolbox of equation-free functions in Matlab/Octave for efficient system level simulation

被引:0
|
作者
John Maclean
J. E. Bunder
A. J. Roberts
机构
[1] University of Adelaide,School of Mathematical Sciences
来源
Numerical Algorithms | 2021年 / 87卷
关键词
Multiscale methods; Code toolbox; Numerical algorithms;
D O I
暂无
中图分类号
学科分类号
摘要
The ‘equation-free toolbox’ empowers the computer-assisted analysis of complex, multiscale systems. Its aim is to enable scientists and engineers to immediately use microscopic simulators to perform macro-scale system level tasks and analysis, because micro-scale simulations are often the best available description of a system. The methodology bypasses the derivation of macroscopic evolution equations by computing the micro-scale simulator only over short bursts in time on small patches in space, with bursts and patches well-separated in time and space respectively. We introduce the suite of coded equation-free functions in an accessible way, link to more detailed descriptions, discuss their mathematical support, and introduce a novel and efficient algorithm for Projective Integration. Some facets of toolbox development of equation-free functions are then detailed. Download the toolbox functions and use to empower efficient and accurate simulation in a wide range of science and engineering problems.
引用
收藏
页码:1729 / 1748
页数:19
相关论文
共 50 条
  • [1] A toolbox of equation-free functions in Matlab/Octave for efficient system level simulation
    Maclean, John
    Bunder, J. E.
    Roberts, A. J.
    [J]. NUMERICAL ALGORITHMS, 2021, 87 (04) : 1729 - 1748
  • [2] Free web-based simulation toolbox for efficient control design with MATLAB
    Mann, H
    Sevcenko, M
    Pavlík, J
    [J]. MODELLING AND SIMULATION 2001, 2001, : 489 - 493
  • [3] Equation-free system-level dynamic modeling and analysis in energy processing
    Stankovic, Aleksandar M.
    [J]. 2007 39TH NORTH AMERICAN POWER SYMPOSIUM, VOLS 1 AND 2, 2007, : 461 - 467
  • [4] Variance reduction for the equation-free simulation of multiscale stochastic systems
    Papavasiliou, Anastasia
    Kevrekidis, Ioannis G.
    [J]. MULTISCALE MODELING & SIMULATION, 2007, 6 (01): : 70 - 89
  • [5] ADAPTIVELY DETECT AND ACCURATELY RESOLVE MACRO-SCALE SHOCKS IN AN EFFICIENT EQUATION-FREE MULTISCALE SIMULATION
    Maclean, J. O. H. N.
    Bunder, J. E.
    Kevrekidis, I. G.
    Roberts, A. J.
    [J]. SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2022, 44 (04): : A2557 - A2581
  • [6] Equation-free dynamic modelling of power system and detection of bifurcation point
    Mumbaikar, U.
    Wang, G.
    Bhil, S.
    Singh, N.
    Stankovic, A. M.
    [J]. 2016 North American Power Symposium (NAPS), 2016,
  • [7] 5G System Level Simulation Calibration Using MATLAB 5G Toolbox
    Xue, Yu
    Zhai, Yuxiao
    Sousa, Elvino
    Li, Wei
    Zhang, Liang
    Hong, Zhihong
    Wu, Yiyan
    [J]. 2022 IEEE INTERNATIONAL SYMPOSIUM ON BROADBAND MULTIMEDIA SYSTEMS AND BROADCASTING (BMSB), 2022,
  • [8] Using Equation-Free Computation to Accelerate Network-Free Stochastic Simulation of Chemical Kinetics
    Lin, Yen Ting
    Chylek, Lily A.
    Lemons, Nathan W.
    Hlavacek, William S.
    [J]. JOURNAL OF PHYSICAL CHEMISTRY B, 2018, 122 (24): : 6351 - 6356
  • [9] UNCERTAINTY QUANTIFICATION FOR ATOMISTIC REACTION MODELS: AN EQUATION-FREE STOCHASTIC SIMULATION ALGORITHM EXAMPLE
    Zou, Yu
    Kevrekidis, Ioannis G.
    [J]. MULTISCALE MODELING & SIMULATION, 2008, 6 (04): : 1217 - 1233
  • [10] MatDyn, A New Matlab Based Toolbox for Power System Dynamic Simulation
    Cole, Stijn
    Belmans, Ronnie
    [J]. 2011 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING, 2011,