Convolutional-network models to predict wall-bounded turbulence from wall quantities
Two models based on convolutional neural networks are trained to predict the two-dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a turbulent open-channel flow, using the wall-shear-stress components and the wall pressure as inputs. The first model is a ful...
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| Veröffentlicht in: | Journal of fluid mechanics Jg. 928 |
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| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Cambridge, UK
Cambridge University Press
10.12.2021
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| ISSN: | 0022-1120, 1469-7645, 1469-7645 |
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| Abstract | Two models based on convolutional neural networks are trained to predict the two-dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a turbulent open-channel flow, using the wall-shear-stress components and the wall pressure as inputs. The first model is a fully convolutional neural network (FCN) which directly predicts the fluctuations, while the second one reconstructs the flow fields using a linear combination of orthonormal basis functions, obtained through proper orthogonal decomposition (POD), and is hence named FCN-POD. Both models are trained using data from direct numerical simulations at friction Reynolds numbers $Re_{\tau } = 180$ and 550. Being able to predict the nonlinear interactions in the flow, both models show better predictions than the extended proper orthogonal decomposition (EPOD), which establishes a linear relation between the input and output fields. The performance of the models is compared based on predictions of the instantaneous fluctuation fields, turbulence statistics and power-spectral densities. FCN exhibits the best predictions closer to the wall, whereas FCN-POD provides better predictions at larger wall-normal distances. We also assessed the feasibility of transfer learning for the FCN model, using the model parameters learned from the $Re_{\tau }=180$ dataset to initialize those of the model that is trained on the $Re_{\tau }=550$ dataset. After training the initialized model at the new $Re_{\tau }$, our results indicate the possibility of matching the reference-model performance up to $y^{+}=50$, with $50\,\%$ and $25\,\%$ of the original training data. We expect that these non-intrusive sensing models will play an important role in applications related to closed-loop control of wall-bounded turbulence. |
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| AbstractList | Two models based on convolutional neural networks are trained to predict the two-dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a turbulent open-channel flow, using the wall-shear-stress components and the wall pressure as inputs. The first model is a fully convolutional neural network (FCN) which directly predicts the fluctuations, while the second one reconstructs the flow fields using a linear combination of orthonormal basis functions, obtained through proper orthogonal decomposition (POD), and is hence named FCN-POD. Both models are trained using data from direct numerical simulations at friction Reynolds numbers
$Re_{\tau } = 180$
and 550. Being able to predict the nonlinear interactions in the flow, both models show better predictions than the extended proper orthogonal decomposition (EPOD), which establishes a linear relation between the input and output fields. The performance of the models is compared based on predictions of the instantaneous fluctuation fields, turbulence statistics and power-spectral densities. FCN exhibits the best predictions closer to the wall, whereas FCN-POD provides better predictions at larger wall-normal distances. We also assessed the feasibility of transfer learning for the FCN model, using the model parameters learned from the
$Re_{\tau }=180$
dataset to initialize those of the model that is trained on the
$Re_{\tau }=550$
dataset. After training the initialized model at the new
$Re_{\tau }$
, our results indicate the possibility of matching the reference-model performance up to
$y^{+}=50$
, with
$50\,\%$
and
$25\,\%$
of the original training data. We expect that these non-intrusive sensing models will play an important role in applications related to closed-loop control of wall-bounded turbulence. Two models based on convolutional neural networks are trained to predict the two-dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a turbulent open-channel flow, using the wall-shear-stress components and the wall pressure as inputs. The first model is a fully convolutional neural network (FCN) which directly predicts the fluctuations, while the second one reconstructs the flow fields using a linear combination of orthonormal basis functions, obtained through proper orthogonal decomposition (POD), and is hence named FCN-POD. Both models are trained using data from direct numerical simulations at friction Reynolds numbers \(Re_{\tau } = 180\) and 550. Being able to predict the nonlinear interactions in the flow, both models show better predictions than the extended proper orthogonal decomposition (EPOD), which establishes a linear relation between the input and output fields. The performance of the models is compared based on predictions of the instantaneous fluctuation fields, turbulence statistics and power-spectral densities. FCN exhibits the best predictions closer to the wall, whereas FCN-POD provides better predictions at larger wall-normal distances. We also assessed the feasibility of transfer learning for the FCN model, using the model parameters learned from the \(Re_{\tau }=180\) dataset to initialize those of the model that is trained on the \(Re_{\tau }=550\) dataset. After training the initialized model at the new \(Re_{\tau }\), our results indicate the possibility of matching the reference-model performance up to \(y^{+}=50\), with \(50\,\%\) and \(25\,\%\) of the original training data. We expect that these non-intrusive sensing models will play an important role in applications related to closed-loop control of wall-bounded turbulence. Two models based on convolutional neural networks are trained to predict the two-dimensional instantaneous velocity-fluctuation fields at different wall-normal locations in a turbulent open-channel flow, using the wall-shear-stress components and the wall pressure as inputs. The first model is a fully convolutional neural network (FCN) which directly predicts the fluctuations, while the second one reconstructs the flow fields using a linear combination of orthonormal basis functions, obtained through proper orthogonal decomposition (POD), and is hence named FCN-POD. Both models are trained using data from direct numerical simulations at friction Reynolds numbers Re-tau = 180 and 550. Being able to predict the nonlinear interactions in the flow, both models show better predictions than the extended proper orthogonal decomposition (EPOD), which establishes a linear relation between the input and output fields. The performance of the models is compared based on predictions of the instantaneous fluctuation fields, turbulence statistics and power-spectral densities. FCN exhibits the best predictions closer to the wall, whereas FCN-POD provides better predictions at larger wall-normal distances. We also assessed the feasibility of transfer learning for the FCN model, using the model parameters learned from the Re-tau = 180 dataset to initialize those of the model that is trained on the Re-tau = 550 dataset. After training the initialized model at the new Ret, our results indicate the possibility of matching the reference-model performance up to y(+) = 50, with 50% and 25% of the original training data. We expect that these non-intrusive sensing models will play an important role in applications related to closed-loop control of wall-bounded turbulence. |
| ArticleNumber | A27 |
| Author | Guastoni, Luca Ianiro, Andrea Vinuesa, Ricardo Schlatter, Philipp Güemes, Alejandro Discetti, Stefano Azizpour, Hossein |
| Author_xml | – sequence: 1 givenname: Luca orcidid: 0000-0002-8589-1572 surname: Guastoni fullname: Guastoni, Luca email: guastoni@mech.kth.se organization: 1SimEx/FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden – sequence: 2 givenname: Alejandro orcidid: 0000-0002-1673-9956 surname: Güemes fullname: Güemes, Alejandro organization: 3Aerospace Engineering Research Group, Universidad Carlos III de Madrid, 28911 Leganés, Spain – sequence: 3 givenname: Andrea orcidid: 0000-0001-7342-4814 surname: Ianiro fullname: Ianiro, Andrea organization: 3Aerospace Engineering Research Group, Universidad Carlos III de Madrid, 28911 Leganés, Spain – sequence: 4 givenname: Stefano orcidid: 0000-0001-9025-1505 surname: Discetti fullname: Discetti, Stefano organization: 3Aerospace Engineering Research Group, Universidad Carlos III de Madrid, 28911 Leganés, Spain – sequence: 5 givenname: Philipp orcidid: 0000-0001-9627-5903 surname: Schlatter fullname: Schlatter, Philipp organization: 1SimEx/FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden – sequence: 6 givenname: Hossein orcidid: 0000-0001-5211-6388 surname: Azizpour fullname: Azizpour, Hossein organization: 2Swedish e-Science Research Centre (SeRC), SE-100 44 Stockholm, Sweden – sequence: 7 givenname: Ricardo orcidid: 0000-0001-6570-5499 surname: Vinuesa fullname: Vinuesa, Ricardo organization: 1SimEx/FLOW, Engineering Mechanics, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden |
| BackLink | https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-305768$$DView record from Swedish Publication Index (Kungliga Tekniska Högskolan) |
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| Cites_doi | 10.1017/jfm.2013.494 10.1016/0893-6080(89)90014-2 10.1103/PhysRevFluids.4.114603 10.1126/sciadv.aay2631 10.1109/5.726791 10.1126/science.aaf7894 10.1017/jfm.2017.580 10.1103/PhysRevFluids.4.054603 10.1017/jfm.2011.524 10.1090/qam/910463 10.1017/S0022112088001442 10.1063/5.0058346 10.1017/S0022112001005808 10.1063/1.869290 10.1063/5.0006492 10.1016/j.expthermflusci.2019.02.001 10.1016/0893-6080(88)90014-7 10.1017/jfm.2016.803 10.1016/0893-6080(89)90020-8 10.1007/BF00332918 10.1038/s41467-019-14108-y 10.1126/science.aaw4741 10.1017/jfm.2019.822 10.1038/nature14539 10.1016/j.physd.2008.10.009 10.1017/jfm.2019.801 10.1017/jfm.2020.948 10.1080/14685248.2020.1757685 10.1016/j.jcp.2019.108910 10.1017/jfm.2019.27 10.1017/jfm.2014.553 10.1017/S0022112008003601 10.1017/jfm.2019.814 10.1073/pnas.1719367115 10.1016/j.combustflame.2019.02.019 10.1063/1.5128053 10.1017/jfm.2019.238 10.1006/jcph.2002.7146 10.1109/TKDE.2009.191 10.1016/j.ijheatfluidflow.2021.108816 10.1103/PhysRevLett.99.114504 10.1017/jfm.2019.62 10.1017/jfm.2016.615 10.1038/s41591-018-0107-6 10.1162/neco.1989.1.4.541 10.1007/BF00344251 10.1103/PhysRevLett.124.010508 10.1038/s41586-019-1559-7 10.1007/s00348-003-0656-3 10.1063/1.4879255 10.1017/jfm.2020.409 10.1103/PhysRevFluids.3.074602 10.1016/j.jcp.2018.10.045 10.1109/CVPR.2015.7298965 10.1017/jfm.2018.660 10.1109/TRPMS.2021.3066428 10.1016/j.expthermflusci.2017.12.011 10.1103/PhysRevFluids.2.034603 10.1017/jfm.2018.770 10.1007/s00348-006-0199-5 10.1088/1367-2630/6/1/056 10.1103/PhysRevFluids.4.064603 10.1088/1873-7005/aaca81 10.1017/jfm.2016.345 10.1017/S0022112094000431 10.1016/j.cpc.2017.05.023 10.1063/1.5127202 10.1146/annurev-fluid-010518-040547 10.1063/1.4873199 10.1126/science.1127647 10.1146/annurev-fluid-010719-060214 |
| ContentType | Journal Article |
| Copyright | The Author(s), 2021. Published by Cambridge University Press. The Author(s), 2021. Published by Cambridge University Press. This work is licensed under the Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s), 2021. Published by Cambridge University Press. – notice: The Author(s), 2021. Published by Cambridge University Press. This work is licensed under the Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| DOI | 10.1017/jfm.2021.812 |
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| Keywords | turbulence simulation |
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| References | 2017; 2 2019; 51 1988; 190 2019; 203 2014; 26 2004; 6 2020; 882 2020; 124 2020; 367 2020; 11 1997; 9 2017; 830 2009; 238 2020; 6 1980; 36 1987; 45 2002; 182 2018; 3 2012; 694 2021; 33 2018; 854 2020; 52 1994; 262 2020; 897 2016; 353 2016; 798 2021; 909 2019; 870 2019; 398 2019; 475 1989; 2 2017; 219 2009; 22 2017; 814 2019; 4 2014; 758 1989; 1 2019; 31 2015; 521 2016; 807 1988; 59 2003; 35 2019; 104 2019; 864 2019; 865 2020; 32 2006; 313 2001; 448 2007; 99 2021; 90 2018; 24 1988; 1 2006; 41 2013; 736 2019; 858 2018; 115 2008; 615 2019; 378 2018; 93 2020; 476 2020; 21 2019; 573 2020; 1522 S0022112021008120_ref27 S0022112021008120_ref72 S0022112021008120_ref73 S0022112021008120_ref70 S0022112021008120_ref71 S0022112021008120_ref76 S0022112021008120_ref32 S0022112021008120_ref77 S0022112021008120_ref33 S0022112021008120_ref30 S0022112021008120_ref74 S0022112021008120_ref31 S0022112021008120_ref75 S0022112021008120_ref36 S0022112021008120_ref37 S0022112021008120_ref78 S0022112021008120_ref34 S0022112021008120_ref79 S0022112021008120_ref35 S0022112021008120_ref38 S0022112021008120_ref39 S0022112021008120_ref80 S0022112021008120_ref83 S0022112021008120_ref40 S0022112021008120_ref84 S0022112021008120_ref81 S0022112021008120_ref82 S0022112021008120_ref43 S0022112021008120_ref44 S0022112021008120_ref85 S0022112021008120_ref41 S0022112021008120_ref42 S0022112021008120_ref86 S0022112021008120_ref47 S0022112021008120_ref48 S0022112021008120_ref45 S0022112021008120_ref46 S0022112021008120_ref49 Guastoni (S0022112021008120_ref29) 2020; 1522 S0022112021008120_ref50 S0022112021008120_ref51 S0022112021008120_ref54 S0022112021008120_ref11 S0022112021008120_ref55 S0022112021008120_ref52 S0022112021008120_ref53 S0022112021008120_ref14 S0022112021008120_ref58 S0022112021008120_ref59 S0022112021008120_ref15 S0022112021008120_ref12 S0022112021008120_ref56 S0022112021008120_ref13 S0022112021008120_ref57 Goodfellow (S0022112021008120_ref28) 2016 S0022112021008120_ref18 S0022112021008120_ref19 S0022112021008120_ref16 S0022112021008120_ref17 Erichson (S0022112021008120_ref21) 2020; 476 S0022112021008120_ref2 S0022112021008120_ref1 S0022112021008120_ref61 S0022112021008120_ref62 S0022112021008120_ref60 S0022112021008120_ref65 S0022112021008120_ref22 S0022112021008120_ref66 S0022112021008120_ref9 S0022112021008120_ref8 S0022112021008120_ref63 S0022112021008120_ref7 S0022112021008120_ref64 S0022112021008120_ref20 S0022112021008120_ref6 S0022112021008120_ref69 S0022112021008120_ref25 S0022112021008120_ref26 S0022112021008120_ref5 S0022112021008120_ref23 S0022112021008120_ref67 S0022112021008120_ref4 S0022112021008120_ref24 Bucci (S0022112021008120_ref10) 2019; 475 S0022112021008120_ref68 S0022112021008120_ref3 |
| References_xml | – volume: 378 start-page: 686 year: 2019 end-page: 707 article-title: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations publication-title: J. Comput. Phys. – volume: 521 start-page: 436 issue: 7553 year: 2015 end-page: 444 article-title: Deep learning publication-title: Nature – volume: 573 start-page: 568 year: 2019 end-page: 572 article-title: Deep learning for multi-year ENSO forecasts publication-title: Nature – volume: 882 start-page: A13 year: 2020 article-title: Nonlinear mode decomposition with convolutional neural networks for fluid dynamics publication-title: J. Fluid Mech. – volume: 1522 start-page: 012022 year: 2020 article-title: Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks publication-title: J. Phys.: Conf. Ser. – volume: 45 start-page: 573 issue: 3 year: 1987 end-page: 582 article-title: Turbulence and the dynamics of coherent structures. II. Symmetries and transformations publication-title: Q. Appl. Maths – volume: 11 start-page: 233 year: 2020 article-title: The role of artificial intelligence in achieving the sustainable development goals publication-title: Nat. Commun. – volume: 353 start-page: 790 year: 2016 end-page: 794 article-title: Combining satellite imagery and machine learning to predict poverty publication-title: Science – volume: 694 start-page: 100 year: 2012 end-page: 130 article-title: The three-dimensional structure of momentum transfer in turbulent channels publication-title: J. Fluid Mech. – volume: 238 start-page: 290 year: 2009 end-page: 308 article-title: Predictive flow-field estimation publication-title: Physica D – volume: 475 start-page: 20190351 year: 2019 article-title: Control of chaotic systems by deep reinforcement learning publication-title: Proc. R. Soc. Lond. A – volume: 32 start-page: 053605 issue: 5 year: 2020 article-title: Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning publication-title: Phys. Fluids – volume: 870 start-page: 106 year: 2019 end-page: 120 article-title: Super-resolution reconstruction of turbulent flows with machine learning publication-title: J. Fluid Mech. – volume: 203 start-page: 255 year: 2019 article-title: Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates publication-title: Combust. Flame – volume: 6 start-page: 1 year: 2020 end-page: 16 article-title: AI Feynman: a physics-inspired method for symbolic regression publication-title: Sci. Adv. – volume: 22 start-page: 1345 issue: 10 year: 2009 end-page: 1359 article-title: A survey on transfer learning publication-title: IEEE Trans. Knowl. Data Engng – volume: 41 start-page: 763 issue: 5 year: 2006 end-page: 775 article-title: On spectral linear stochastic estimation publication-title: Exp. Fluids – volume: 190 start-page: 531 year: 1988 end-page: 559 article-title: Stochastic estimation of organized turbulent structure: homogeneous shear flow publication-title: J. Fluid Mech. – volume: 93 start-page: 119 year: 2018 end-page: 130 article-title: Estimation of time-resolved turbulent fields through correlation of non-time-resolved field measurements and time-resolved point measurements publication-title: Exp. Therm. Fluid Sci. – volume: 909 start-page: A9 year: 2021 article-title: Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows publication-title: J. Fluid Mech. – volume: 26 start-page: 045113 issue: 4 year: 2014 article-title: A study of energetic large-scale structures in turbulent boundary layer publication-title: Phys. Fluids – volume: 4 start-page: 114603 year: 2019 article-title: Logarithmic-layer turbulence: a view from the wall publication-title: Phys. Rev. Fluids – volume: 182 start-page: 1 issue: 1 year: 2002 end-page: 26 article-title: Neural network modeling for near wall turbulent flow publication-title: J. Comput. Phys. – volume: 21 start-page: 567 year: 2020 end-page: 584 article-title: A perspective on machine learning in turbulent flows publication-title: J. Turbul. – volume: 24 start-page: 1342 year: 2018 end-page: 1350 article-title: Clinically applicable deep learning for diagnosis and referral in retinal disease publication-title: Nat. Med. – volume: 36 start-page: 193 issue: 4 year: 1980 end-page: 202 article-title: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position publication-title: Biol. Cybern. – volume: 448 start-page: 53 year: 2001 end-page: 80 article-title: Large-scale modes of turbulent channel flow: transport and structure publication-title: J. Fluid Mech. – volume: 882 start-page: A2 year: 2020 article-title: Causality of energy-containing eddies in wall turbulence publication-title: J. Fluid Mech. – volume: 858 start-page: 122 year: 2019 end-page: 144 article-title: Subgrid modelling for two-dimensional turbulence using neural networks publication-title: J. Fluid Mech. – volume: 104 start-page: 1 year: 2019 end-page: 8 article-title: Characterization of very-large-scale motions in high-Re pipe flows publication-title: Exp. Therm. Fluid Sci. – volume: 35 start-page: 188 issue: 2 year: 2003 end-page: 192 article-title: Extended proper orthogonal decomposition: a tool to analyse correlated events in turbulent flows publication-title: Exp. Fluids – volume: 6 start-page: 56 year: 2004 article-title: A low-dimensional model for turbulent shear flows publication-title: New J. Phys. – volume: 2 start-page: 53 issue: 1 year: 1989 end-page: 58 article-title: Neural networks and principal component analysis: learning from examples without local minima publication-title: Neural Networks – volume: 115 start-page: E5716 year: 2018 end-page: E5725 article-title: Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning publication-title: Proc. Natl Acad. Sci. USA – volume: 59 start-page: 291 issue: 4–5 year: 1988 end-page: 294 article-title: Auto-association by multilayer perceptrons and singular value decomposition publication-title: Biol. Cybern. – volume: 90 start-page: 108816 year: 2021 article-title: Recurrent neural networks and Koopman-based frameworks for temporal predictions in a low-order model of turbulence publication-title: Intl J. Heat Fluid Flow – volume: 26 start-page: 055112 issue: 5 year: 2014 article-title: Proper orthogonal decomposition-based spectral higher-order stochastic estimation publication-title: Phys. Fluids – volume: 476 start-page: 20200097 year: 2020 article-title: Shallow neural networks for fluid flow reconstruction with limited sensors publication-title: Proc. R. Soc. Lond. A – volume: 1 start-page: 119 issue: 2 year: 1988 end-page: 130 article-title: Neocognitron: a hierarchical neural network capable of visual pattern recognition publication-title: Neural Networks – volume: 1 start-page: 541 issue: 4 year: 1989 end-page: 551 article-title: Backpropagation applied to handwritten ZIP code recognition publication-title: Neural Comput. – volume: 313 start-page: 504 issue: 5786 year: 2006 end-page: 507 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – volume: 798 start-page: 717 year: 2016 end-page: 750 article-title: Modal energy flow analysis of a highly modulated wake behind a wall-mounted pyramid publication-title: J. Fluid Mech. – volume: 615 start-page: 53 year: 2008 end-page: 92 article-title: Low-dimensional characteristics of a transonic jet. Part 2. Estimate and far-field prediction publication-title: J. Fluid Mech. – volume: 2 start-page: 359 issue: 5 year: 1989 end-page: 366 article-title: Multilayer feedforward networks are universal approximators publication-title: Neural Networks – volume: 882 start-page: A18 year: 2020 article-title: Prediction of turbulent heat transfer using convolutional neural networks publication-title: J. Fluid Mech. – volume: 736 start-page: 316 year: 2013 end-page: 350 article-title: Generalized phase average with applications to sensor-based flow estimation of the wall-mounted square cylinder wake publication-title: J. Fluid Mech. – volume: 51 start-page: 011408 year: 2019 article-title: Quantification of amplitude modulation in wall-bounded turbulence publication-title: Fluid Dyn. Res. – volume: 4 start-page: 064603 year: 2019 article-title: Synthetic turbulent inflow generator using machine learning publication-title: Phys. Rev. Fluids – volume: 3 start-page: 074602 year: 2018 article-title: Physics-informed machine learning approach for augmenting turbulence models: a comprehensive framework publication-title: Phys. Rev. Fluids – volume: 51 start-page: 357 year: 2019 end-page: 377 article-title: Turbulence modeling in the age of data publication-title: Annu. Rev. Fluid Mech. – volume: 99 start-page: 114504 year: 2007 article-title: Reynolds number invariance of the structure inclination angle in wall turbulence publication-title: Phys. Rev. Lett. – volume: 52 start-page: 477 year: 2020 end-page: 508 article-title: Machine learning for fluid mechanics publication-title: Annu. Rev. Fluid Mech. – volume: 897 start-page: R1 year: 2020 article-title: Leveraging reduced-order models for state estimation using deep learning publication-title: J. Fluid Mech. – volume: 865 start-page: 281 year: 2019 end-page: 302 article-title: Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control publication-title: J. Fluid Mech. – volume: 398 start-page: 108910 year: 2019 article-title: Deep neural networks for data-driven LES closure models publication-title: J. Comput. Phys. – volume: 2 start-page: 034603 issue: 3 year: 2017 article-title: Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data publication-title: Phys. Rev. Fluids – volume: 854 start-page: R1 year: 2018 article-title: Machine-aided turbulence theory publication-title: J. Fluid Mech. – volume: 367 start-page: 1026 year: 2020 end-page: 1030 article-title: Hidden fluid mechanics: learning velocity and pressure fields from flow visualizations publication-title: Science – volume: 124 start-page: 010508 year: 2020 article-title: Discovering physical concepts with neural networks publication-title: Phys. Rev. Lett. – volume: 33 start-page: 075121 issue: 7 year: 2021 article-title: From coarse wall measurements to turbulent velocity fields through deep learning publication-title: Phys. Fluids – volume: 219 start-page: 236 year: 2017 end-page: 245 article-title: A performance analysis of ensemble averaging for high fidelity turbulence simulations at the strong scaling limit publication-title: Comput. Phys. Commun. – volume: 807 start-page: 155 year: 2016 end-page: 166 article-title: Reynolds averaged turbulence modelling using deep neural networks with embedded invariance publication-title: J. Fluid Mech. – volume: 758 start-page: 728 year: 2014 end-page: 753 article-title: A dynamic observer to capture and control perturbation energy in noise amplifiers publication-title: J. Fluid Mech. – volume: 864 start-page: 708 year: 2019 end-page: 745 article-title: Transfer functions for flow predictions in wall-bounded turbulence publication-title: J. Fluid Mech. – volume: 4 start-page: 054603 issue: 5 year: 2019 article-title: Predictions of turbulent shear flows using deep neural networks publication-title: Phys. Rev. Fluids – volume: 814 start-page: 1 year: 2017 end-page: 4 article-title: Deep learning in fluid dynamics publication-title: J. Fluid Mech. – volume: 830 start-page: 760 year: 2017 end-page: 796 article-title: Estimation of turbulent channel flow at $Re_\theta =100$ based on the wall measurement using a simple sequential approach publication-title: J. Fluid Mech. – volume: 262 start-page: 75 year: 1994 end-page: 110 article-title: Active turbulence control for drag reduction in wall-bounded flows publication-title: J. Fluid Mech. – volume: 31 start-page: 125112 issue: 12 year: 2019 article-title: Sensing the turbulent large-scale motions with their wall signature publication-title: Phys. Fluids – volume: 32 start-page: 015108 issue: 1 year: 2020 article-title: Machine learning strategies applied to the control of a fluidic pinball publication-title: Phys. Fluids – volume: 9 start-page: 1740 year: 1997 end-page: 1747 article-title: Application of neural networks to turbulence control for drag reduction publication-title: Phys. Fluids – ident: S0022112021008120_ref7 doi: 10.1017/jfm.2013.494 – ident: S0022112021008120_ref66 – ident: S0022112021008120_ref4 doi: 10.1016/0893-6080(89)90014-2 – ident: S0022112021008120_ref56 – ident: S0022112021008120_ref20 doi: 10.1103/PhysRevFluids.4.114603 – ident: S0022112021008120_ref82 doi: 10.1126/sciadv.aay2631 – ident: S0022112021008120_ref49 doi: 10.1109/5.726791 – ident: S0022112021008120_ref41 doi: 10.1126/science.aaf7894 – volume: 476 start-page: 20200097 year: 2020 ident: S0022112021008120_ref21 article-title: Shallow neural networks for fluid flow reconstruction with limited sensors publication-title: Proc. R. Soc. Lond. A – ident: S0022112021008120_ref78 doi: 10.1017/jfm.2017.580 – ident: S0022112021008120_ref77 doi: 10.1103/PhysRevFluids.4.054603 – ident: S0022112021008120_ref55 doi: 10.1017/jfm.2011.524 – ident: S0022112021008120_ref76 doi: 10.1090/qam/910463 – ident: S0022112021008120_ref2 doi: 10.1017/S0022112088001442 – ident: S0022112021008120_ref32 doi: 10.1063/5.0058346 – ident: S0022112021008120_ref38 – ident: S0022112021008120_ref52 doi: 10.1017/S0022112001005808 – ident: S0022112021008120_ref50 doi: 10.1063/1.869290 – ident: S0022112021008120_ref79 doi: 10.1063/5.0006492 – ident: S0022112021008120_ref14 doi: 10.1016/j.expthermflusci.2019.02.001 – ident: S0022112021008120_ref27 doi: 10.1016/0893-6080(88)90014-7 – ident: S0022112021008120_ref45 doi: 10.1017/jfm.2016.803 – ident: S0022112021008120_ref36 doi: 10.1016/0893-6080(89)90020-8 – ident: S0022112021008120_ref17 – ident: S0022112021008120_ref8 doi: 10.1007/BF00332918 – ident: S0022112021008120_ref30 – ident: S0022112021008120_ref83 doi: 10.1038/s41467-019-14108-y – ident: S0022112021008120_ref1 – ident: S0022112021008120_ref74 doi: 10.1126/science.aaw4741 – ident: S0022112021008120_ref64 doi: 10.1017/jfm.2019.822 – ident: S0022112021008120_ref47 doi: 10.1038/nature14539 – ident: S0022112021008120_ref63 doi: 10.1016/j.physd.2008.10.009 – ident: S0022112021008120_ref54 doi: 10.1017/jfm.2019.801 – ident: S0022112021008120_ref24 doi: 10.1017/jfm.2020.948 – ident: S0022112021008120_ref70 doi: 10.1080/14685248.2020.1757685 – ident: S0022112021008120_ref5 doi: 10.1016/j.jcp.2019.108910 – ident: S0022112021008120_ref75 doi: 10.1017/jfm.2019.27 – ident: S0022112021008120_ref33 doi: 10.1017/jfm.2014.553 – ident: S0022112021008120_ref81 doi: 10.1017/S0022112008003601 – ident: S0022112021008120_ref43 doi: 10.1017/jfm.2019.814 – ident: S0022112021008120_ref67 doi: 10.1073/pnas.1719367115 – ident: S0022112021008120_ref68 – volume-title: Deep Learning year: 2016 ident: S0022112021008120_ref28 – ident: S0022112021008120_ref46 doi: 10.1016/j.combustflame.2019.02.019 – ident: S0022112021008120_ref31 doi: 10.1063/1.5128053 – ident: S0022112021008120_ref23 doi: 10.1017/jfm.2019.238 – ident: S0022112021008120_ref61 doi: 10.1006/jcph.2002.7146 – ident: S0022112021008120_ref69 doi: 10.1109/TKDE.2009.191 – ident: S0022112021008120_ref19 doi: 10.1016/j.ijheatfluidflow.2021.108816 – ident: S0022112021008120_ref59 doi: 10.1103/PhysRevLett.99.114504 – ident: S0022112021008120_ref71 doi: 10.1017/jfm.2019.62 – ident: S0022112021008120_ref51 doi: 10.1017/jfm.2016.615 – ident: S0022112021008120_ref13 doi: 10.1038/s41591-018-0107-6 – ident: S0022112021008120_ref48 doi: 10.1162/neco.1989.1.4.541 – ident: S0022112021008120_ref26 doi: 10.1007/BF00344251 – ident: S0022112021008120_ref39 doi: 10.1103/PhysRevLett.124.010508 – ident: S0022112021008120_ref34 doi: 10.1038/s41586-019-1559-7 – ident: S0022112021008120_ref6 doi: 10.1007/s00348-003-0656-3 – ident: S0022112021008120_ref44 – ident: S0022112021008120_ref3 doi: 10.1063/1.4879255 – ident: S0022112021008120_ref65 doi: 10.1017/jfm.2020.409 – ident: S0022112021008120_ref40 – ident: S0022112021008120_ref85 doi: 10.1103/PhysRevFluids.3.074602 – ident: S0022112021008120_ref73 doi: 10.1016/j.jcp.2018.10.045 – ident: S0022112021008120_ref53 doi: 10.1109/CVPR.2015.7298965 – volume: 475 start-page: 20190351 year: 2019 ident: S0022112021008120_ref10 article-title: Control of chaotic systems by deep reinforcement learning publication-title: Proc. R. Soc. Lond. A – ident: S0022112021008120_ref42 doi: 10.1017/jfm.2018.660 – ident: S0022112021008120_ref22 doi: 10.1109/TRPMS.2021.3066428 – ident: S0022112021008120_ref15 doi: 10.1016/j.expthermflusci.2017.12.011 – ident: S0022112021008120_ref84 doi: 10.1103/PhysRevFluids.2.034603 – ident: S0022112021008120_ref60 doi: 10.1017/jfm.2018.770 – ident: S0022112021008120_ref80 doi: 10.1007/s00348-006-0199-5 – ident: S0022112021008120_ref57 – ident: S0022112021008120_ref62 doi: 10.1088/1367-2630/6/1/056 – volume: 1522 start-page: 012022 year: 2020 ident: S0022112021008120_ref29 article-title: Prediction of wall-bounded turbulence from wall quantities using convolutional neural networks publication-title: J. Phys.: Conf. Ser. – ident: S0022112021008120_ref25 doi: 10.1103/PhysRevFluids.4.064603 – ident: S0022112021008120_ref11 – ident: S0022112021008120_ref16 doi: 10.1088/1873-7005/aaca81 – ident: S0022112021008120_ref37 doi: 10.1017/jfm.2016.345 – ident: S0022112021008120_ref12 doi: 10.1017/S0022112094000431 – ident: S0022112021008120_ref58 doi: 10.1016/j.cpc.2017.05.023 – ident: S0022112021008120_ref72 doi: 10.1063/1.5127202 – ident: S0022112021008120_ref18 doi: 10.1146/annurev-fluid-010518-040547 – ident: S0022112021008120_ref86 doi: 10.1063/1.4873199 – ident: S0022112021008120_ref35 doi: 10.1126/science.1127647 – ident: S0022112021008120_ref9 doi: 10.1146/annurev-fluid-010719-060214 |
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| Title | Convolutional-network models to predict wall-bounded turbulence from wall quantities |
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