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Constructing Neural Network Based Models for Simulating Dynamical Systems

Authors:

Christian Legaard,

Thomas Schranz,

Gerald Schweiger,

Cláudio Gomes,

Alexandros Iosifidis,

Peter LarsenAuthors Info & Claims

ACM Computing Surveys, Volume 55, Issue 11

Article No.: 236, Pages 1 - 34

https://doi.org/10.1145/3567591

Published: 09 February 2023 Publication History

Abstract

Dynamical systems see widespread use in natural sciences like physics, biology, and chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in applying data-driven modeling techniques to solve a wide range of problems in physics and engineering. This article provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.

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Index Terms

Constructing Neural Network Based Models for Simulating Dynamical Systems
1. Applied computing
  1. Physical sciences and engineering
    1. Engineering
    2. Physics
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression
    2. Machine learning approaches
      1. Neural networks
  2. Modeling and simulation
    1. Simulation types and techniques
      1. Continuous models
      2. Continuous simulation

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 11

November 2023

849 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3572825

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 09 February 2023

Online AM: 16 November 2022

Accepted: 04 October 2022

Revised: 23 September 2022

Received: 02 November 2021

Published in CSUR Volume 55, Issue 11

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Data Model Convergence (DMC) initiative via the Laboratory Directed Research and Development (LDRD) investments at Pacific Northwest National Laboratory (PNNL)
Battelle Memorial Institute

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Noordijk BGarcia Gomez Mten Tusscher Kde Ridder Dvan Dijk ASmith R(2024)The rise of scientific machine learning: a perspective on combining mechanistic modelling with machine learning for systems biologyFrontiers in Systems Biology10.3389/fsysb.2024.14079944Online publication date: 2-Aug-2024
https://doi.org/10.3389/fsysb.2024.1407994
NG JDE ALMEIDA GVAKKILAINEN ELARYSHYN YDEMARTINI N(2024)Comparing a linear transfer function-noise model and a neural network to model boiler bank fouling in a kraft recovery boilerTAPPI Journal10.32964/TJ23.7.37423:7(374-384)Online publication date: 29-Jul-2024
https://doi.org/10.32964/TJ23.7.374
Liu XDavid ICombemale BWimmer MChechik MEgyed A(2024)AI Simulation by Digital Twins: Systematic Survey of the State of the Art and a Reference FrameworkProceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems10.1145/3652620.3688253(401-412)Online publication date: 22-Sep-2024
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Filho JPaul SFrohlich A(2024)Comparison of Neural Network Training Approaches That Preserve Physical Properties of Cyber-Physical System2024 XIV Brazilian Symposium on Computing Systems Engineering (SBESC)10.1109/SBESC65055.2024.10771819(1-6)Online publication date: 26-Nov-2024
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