TY - JOUR
T1 - Data-driven and physical-based identification of partial differential equations for multivariable system
AU - Cao, Wenbo
AU - Zhang, Weiwei
N1 - Publisher Copyright:
© 2022 The Author(s)
PY - 2022/2
Y1 - 2022/2
N2 - Data-driven partial differential equation identification is a potential breakthrough to solve the lack of physical equations in complex dynamic systems. However, existing equation identification methods still cannot effectively identify equations from multivariable complex systems. In this work, we combine physical constraints such as dimension and direction of equation with data-driven method, and successfully identify the Navier-Stocks equations from the flow field data of Karman vortex street. This method provides an effective approach to identify partial differential equations of multivariable complex systems.
AB - Data-driven partial differential equation identification is a potential breakthrough to solve the lack of physical equations in complex dynamic systems. However, existing equation identification methods still cannot effectively identify equations from multivariable complex systems. In this work, we combine physical constraints such as dimension and direction of equation with data-driven method, and successfully identify the Navier-Stocks equations from the flow field data of Karman vortex street. This method provides an effective approach to identify partial differential equations of multivariable complex systems.
KW - Data-driven
KW - Dimensional analysis
KW - Multivariable system
KW - Partial differential equation identification
UR - http://www.scopus.com/inward/record.url?scp=85129020041&partnerID=8YFLogxK
U2 - 10.1016/j.taml.2022.100334
DO - 10.1016/j.taml.2022.100334
M3 - 文章
AN - SCOPUS:85129020041
SN - 2095-0349
VL - 12
JO - Theoretical and Applied Mechanics Letters
JF - Theoretical and Applied Mechanics Letters
IS - 2
M1 - 100334
ER -