Perspective on machine learning for advancing fluid mechanics
Top Cited Papers
- 16 October 2019
- journal article
- research article
- Published by American Physical Society (APS) in Physical Review Fluids
- Vol. 4 (10) , 100501
- https://doi.org/10.1103/physrevfluids.4.100501
Abstract
A perspective is presented on how machine learning (ML), with its burgeoning popularity and the increasing availability of portable implementations, might advance fluid mechanics. As with any numerical or experimental method, ML methods have strengths and limitations, which are acknowledged. Their potential impact is high so long as outcomes are held to the long-standing critical standards that should guide studies of flow physics.Funding Information
- National Science Foundation (DMS-1751477)
- Simons Foundation
- U.S. Department of Energy
- National Nuclear Security Administration (DE-NA0002374)
This publication has 20 references indexed in Scilit:
- Data-assisted reduced-order modeling of extreme events in complex dynamical systemsPLOS ONE, 2018
- Efficient collective swimming by harnessing vortices through deep reinforcement learningProceedings of the National Academy of Sciences, 2018
- Prediction of cardiovascular risk factors from retinal fundus photographs via deep learningNature Biomedical Engineering, 2018
- Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS dataPhysical Review Fluids, 2017
- Model Reduction for Flow Analysis and ControlAnnual Review of Fluid Mechanics, 2017
- Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus PhotographsJAMA, 2016
- Machine learning strategies for systems with invariance propertiesJournal of Computational Physics, 2016
- Evaluation of machine learning algorithms for prediction of regions of high Reynolds averaged Navier Stokes uncertaintyPhysics of Fluids, 2015
- On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance PropagationPLOS ONE, 2015
- Long Short-Term MemoryNeural Computation, 1997