TY - JOUR
T1 - Constructing Neural Network Based Models for Simulating Dynamical Systems
AU - Legaard, Christian Møldrup
AU - Schranz, Thomas
AU - Schweiger, Gerald
AU - Drgona, Jan
AU - Falay, Basak
AU - Gomes, Cláudio
AU - Iosifidis, Alexandros
AU - Abkar, Mahdi
AU - Larsen, Peter Gorm
PY - 2023/2
Y1 - 2023/2
N2 - 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.
AB - 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.
KW - Additional Key Words and PhrasesNeural ODEs
KW - physics-based regularization
KW - physics-informed neural networks
UR - http://www.scopus.com/inward/record.url?scp=85151845803&partnerID=8YFLogxK
U2 - 10.1145/3567591
DO - 10.1145/3567591
M3 - Journal article
SN - 0360-0300
VL - 55
SP - 1
EP - 34
JO - ACM Computing Surveys
JF - ACM Computing Surveys
IS - 11
M1 - 236
ER -