Statistical learning for fluid flows: Sparse Fourier divergence-free approximations-Reference-Cited by-同舟云学术

Statistical learning for fluid flows: Sparse Fourier divergence-free approximations

Published:2021-09 Issue:9 Volume:33 Page:097108
ISSN:1070-6631
Container-title:Physics of Fluids
language:en
Short-container-title:Physics of Fluids

Author:

Espath Luis¹^ORCID,Kabanov Dmitry¹^ORCID,Kiessling Jonas²³^ORCID,Tempone Raúl¹⁴⁵^ORCID

Affiliation:

1. Department of Mathematics, RWTH Aachen University, Gebäude-1953 1.OG, Pontdriesch 14-16, 52062 Aachen, Germany

2. H-Ai AB, Box 5216, 102 45 Stockholm, Sweden

3. Institutionen för Matematik, Kungl. Tekniska Högskolan, 100 44 Stockholm, Sweden

4. Alexander von Humboldt Professor in Mathematics for Uncertainty Quantification, RWTH Aachen University, Germany

5. King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Thuwal 23955-6900, Saudi Arabia

Publisher

AIP Publishing

Subject

Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering

Link

https://aip.scitation.org/doi/pdf/10.1063/5.0064862

Reference13 articles.

1. R. Tipireddy P. Perdikaris, P. Stinis, and A. Tartakovsky , “ A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations,” arXiv:1904.04058 (2019).

2. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

3. Machine Learning for Fluid Mechanics

4. R. F. Tempone , “ Approximation and interpolation of divergence-free flows,” Master's thesis ( Udelar, FI, 1999).

5. S. Lowitzsch , “ Approximation and interpolation employing divergence-free radial basis functions with applications,” Ph.D. thesis ( Texas A&M University, 2004).

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