On the choice of physical constraints in artificial neural networks for predicting flow fields

Author:

Puri RishabhORCID,Onishi Junya,Rüttgers Mario,Sarma Rakesh,Tsubokura Makoto,Lintermann Andreas

Publisher

Elsevier BV

Reference49 articles.

1. Principes généraux du mouvement des fluides;Euler;Mém. Acad. Sci. Berl.,1757

2. On the theories of the internal friction of fluids in motion and of the equilibrium and motion of elastic solids;Stokes;Trans. Camb. Phil. Soc.,1845

3. Deep learning to replace, improve, or aid CFD analysis in built environment applications: A review;Calzolari;Build. Environ.,2021

4. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations;Raissi;J. Comput. Phys.,2019

5. Convolutional neural networks for steady flow approximation;Guo,2016

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