Perspective on machine learning for advancing fluid mechanics
- Authors:
- Publication Date:
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1570536
- Grant/Contract Number:
- NA0002374
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Physical Review Fluids
- Additional Journal Information:
- Journal Name: Physical Review Fluids Journal Volume: 4 Journal Issue: 10; Journal ID: ISSN 2469-990X
- Publisher:
- American Physical Society
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Brenner, M. P., Eldredge, J. D., and Freund, J. B. Perspective on machine learning for advancing fluid mechanics. United States: N. p., 2019.
Web. doi:10.1103/PhysRevFluids.4.100501.
Brenner, M. P., Eldredge, J. D., & Freund, J. B. Perspective on machine learning for advancing fluid mechanics. United States. doi:10.1103/PhysRevFluids.4.100501.
Brenner, M. P., Eldredge, J. D., and Freund, J. B. Wed .
"Perspective on machine learning for advancing fluid mechanics". United States. doi:10.1103/PhysRevFluids.4.100501.
@article{osti_1570536,
title = {Perspective on machine learning for advancing fluid mechanics},
author = {Brenner, M. P. and Eldredge, J. D. and Freund, J. B.},
abstractNote = {},
doi = {10.1103/PhysRevFluids.4.100501},
journal = {Physical Review Fluids},
number = 10,
volume = 4,
place = {United States},
year = {2019},
month = {10}
}
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1103/PhysRevFluids.4.100501
DOI: 10.1103/PhysRevFluids.4.100501
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Cited by: 14 works
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Works referenced in this record:
Data-assisted reduced-order modeling of extreme events in complex dynamical systems
journal, May 2018
- Wan, Zhong Yi; Vlachas, Pantelis; Koumoutsakos, Petros
- PLOS ONE, Vol. 13, Issue 5
Efficient collective swimming by harnessing vortices through deep reinforcement learning
journal, May 2018
- Verma, Siddhartha; Novati, Guido; Koumoutsakos, Petros
- Proceedings of the National Academy of Sciences, Vol. 115, Issue 23
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
journal, February 2018
- Poplin, Ryan; Varadarajan, Avinash V.; Blumer, Katy
- Nature Biomedical Engineering, Vol. 2, Issue 3
Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework
journal, July 2018
- Wu, Jin-Long; Xiao, Heng; Paterson, Eric
- Physical Review Fluids, Vol. 3, Issue 7
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
journal, December 2016
- Gulshan, Varun; Peng, Lily; Coram, Marc
- JAMA, Vol. 316, Issue 22
Machine learning the kinematics of spherical particles in fluid flows
journal, October 2018
- Wan, Zhong Yi; Sapsis, Themistoklis P.
- Journal of Fluid Mechanics, Vol. 857
Machine learning-assisted early ignition prediction in a complex flow
journal, August 2019
- Popov, Pavel P.; Buchta, David A.; Anderson, Michael J.
- Combustion and Flame, Vol. 206
Deep learning of vortex-induced vibrations
journal, December 2018
- Raissi, Maziar; Wang, Zhicheng; Triantafyllou, Michael S.
- Journal of Fluid Mechanics, Vol. 861
Machine-aided turbulence theory
journal, August 2018
- Jiménez, Javier
- Journal of Fluid Mechanics, Vol. 854
Application of a self-organizing map to identify the turbulent-boundary-layer interface in a transitional flow
journal, February 2019
- Wu, Zhao; Lee, Jin; Meneveau, Charles
- Physical Review Fluids, Vol. 4, Issue 2
Long Short-Term Memory
journal, November 1997
- Hochreiter, Sepp; Schmidhuber, Jürgen
- Neural Computation, Vol. 9, Issue 8
Machine-Learning-Based Detection of Aerodynamic Disturbances Using Surface Pressure Measurements
journal, September 2019
- Hou, Wei; Darakananda, Darwin; Eldredge, Jeff D.
- AIAA Journal
Machine learning strategies for systems with invariance properties
journal, August 2016
- Ling, Julia; Jones, Reese; Templeton, Jeremy
- Journal of Computational Physics, Vol. 318
Turbulence Modeling in the Age of Data
journal, January 2019
- Duraisamy, Karthik; Iaccarino, Gianluca; Xiao, Heng
- Annual Review of Fluid Mechanics, Vol. 51, Issue 1
Super-resolution reconstruction of turbulent flows with machine learning
journal, May 2019
- Fukami, Kai; Fukagata, Koji; Taira, Kunihiko
- Journal of Fluid Mechanics, Vol. 870
Machine Learning for Fluid Mechanics
journal, September 2019
- Brunton, Steven L.; Noack, Bernd R.; Koumoutsakos, Petros
- Annual Review of Fluid Mechanics, Vol. 52, Issue 1
Model Reduction for Flow Analysis and Control
journal, January 2017
- Rowley, Clarence W.; Dawson, Scott T. M.
- Annual Review of Fluid Mechanics, Vol. 49, Issue 1
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data
journal, March 2017
- Wang, Jian-Xun; Wu, Jin-Long; Xiao, Heng
- Physical Review Fluids, Vol. 2, Issue 3
Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertainty
journal, August 2015
- Ling, J.; Templeton, J.
- Physics of Fluids, Vol. 27, Issue 8
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
journal, July 2015
- Bach, Sebastian; Binder, Alexander; Montavon, Grégoire
- PLOS ONE, Vol. 10, Issue 7