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Document type:
Zeitschriftenaufsatz 
Author(s):
Buhendwa, Aaron B.; Adami, Stefan; Adams, Nikolaus A. 
Title:
Inferring incompressible two-phase flow fields from the interface motion using physics-informed neural networks 
Abstract:
In this work, physics-informed neural networks are applied to incompressible two-phase flow problems. We investigate the forward problem, where the governing equations are solved from initial and boundary conditions, as well as the inverse problem, where continuous velocity and pressure fields are inferred from scattered-time data on the interface position. We employ a volume of fluid approach, i.e. the auxiliary variable here is the volume fraction of the fluids within each phase. For the forwa...    »
 
Keywords:
Physics-informed neural networks; Two-phase flows; Volume-of-fluid; Hidden fluid mechanics; Incompressible Navier–Stokes equations 
Dewey Decimal Classification:
620 Ingenieurwissenschaften 
Journal title:
Machine Learning with Applications 
Year:
2021 
Journal volume:
Pages contribution:
100029 
Language:
en 
Publisher:
Elsevier BV 
E-ISSN:
2666-8270 
Accepted:
10.03.2021 
Date of publication:
01.06.2021 
TUM Institution:
Lehrstuhl für Aerodynamik und Strömungsmechanik