Papers
Topics
Authors
Recent
Search
2000 character limit reached

Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows

Published 26 Jan 2023 in physics.flu-dyn, cs.LG, and physics.comp-ph | (2301.10937v2)

Abstract: This paper surveys machine-learning-based super-resolution reconstruction for vortical flows. Super resolution aims to find the high-resolution flow fields from low-resolution data and is generally an approach used in image reconstruction. In addition to surveying a variety of recent super-resolution applications, we provide case studies of super-resolution analysis for an example of two-dimensional decaying isotropic turbulence. We demonstrate that physics-inspired model designs enable successful reconstruction of vortical flows from spatially limited measurements. We also discuss the challenges and outlooks of machine-learning-based super-resolution analysis for fluid flow applications. The insights gained from this study can be leveraged for super-resolution analysis of numerical and experimental flow data.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (186)
  1. M. Irani and S. Peleg. Improving resolution by image registration. CVGIP: Graphical models and image processing, 53(3):231–239, 1991.
  2. J. Salvador. Example-Based super resolution. Academic Press, 2016.
  3. V. Bannore. Iterative-interpolation super-resolution image reconstruction: a computationally efficient technique, volume 195. Springer Science & Business Media, 2009.
  4. R. Keys. Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust. Speech Signal Process., 29(6):1153–1160, 1981.
  5. A frequency domain approach to registration of aliased images with application to super-resolution. EURASIP J. Adv. Signal Process., 2006:1–14, 2006.
  6. PSF estimation using sharp edge prediction. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1–8. IEEE, 2008.
  7. B. D. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. Proceedings of the DARPA Image Understanding Workshop, 81:674–679, 1981.
  8. T. Michaeli and M. Irani. Nonparametric blind super-resolution. In Proceedings of the IEEE International Conference on Computer Vision, pages 945–952, 2013.
  9. Super-resolution from a single image. In IEEE 12th international conference on computer vision, pages 349–356. IEEE, 2009.
  10. Separating signal from noise using patch recurrence across scales. In IEEE conference on computer vision and pattern recognition, pages 1195–1202, 2013.
  11. Space-time super-resolution from a single video. In CVPR 2011, pages 3353–3360. IEEE Computer Society, 2011.
  12. G. Freedman and R. Fattal. Image and video upscaling from local self-examples. ACM Trans. Graph., 30(2):1–11, 2011.
  13. Fast image super-resolution based on in-place example regression. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1059–1066, 2013.
  14. S. Baker and T. Kanade. Limits on super-resolution and how to break them. IEEE Trans. Pattern Anal. Mach. Intell., 24(9):1167–1183, 2002.
  15. Super-resolution image reconstruction: a technical overview. IEEE Signal Process. Mag., 20(3):21–36, 2003.
  16. Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500):2323–2326, 2000.
  17. Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In British Machine Vision Conference (BMVC), 2012.
  18. Super-resolution through neighbor embedding. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 1, pages I–I. IEEE, 2004.
  19. Example-based super-resolution. IEEE Comput. Graph. Appl., 22(2):56–65, 2002.
  20. Learning low-level vision. Int. J. Comput. Vis., 40(1):25–47, 2000.
  21. Efficient sparse coding algorithms. In Proceedings of the 19th International Conference on Neural Information Processing Systems, pages 801–808, 2006.
  22. Image super-resolution as sparse representation of raw image patches. In IEEE conference on computer vision and pattern recognition, pages 1–8. IEEE, 2008.
  23. Geometry constrained sparse coding for single image super-resolution. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1648–1655. IEEE, 2012.
  24. Image super-resolution via sparse representation. IEEE Trans. Image Process., 19(11):2861–2873, 2010.
  25. Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process., 21(11):4544–4556, 2012.
  26. Learning a deep convolutional network for image super-resolution. In European conference on computer vision, pages 184–199. Springer, 2014.
  27. Accelerating the super-resolution convolutional neural network. In European conference on computer vision, pages 391–407. Springer, 2016.
  28. Deep learning for single image super-resolution: A brief review. IEEE Trans. Multimed., 21(12):3106–3121, 2019.
  29. Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell., 38(2):295–307, 2015.
  30. Machine learning for fluid mechanincs. Annu. Rev. Fluid Mech., 52:477–508, 2020.
  31. Special issue on machine learning and data-driven methods in fluid dynamics. Theor. Comput. Fluid Dyn., 34:333–337, 2020.
  32. Perspective on machine learning for advancing fluid mechanics. Phys. Rev. Fluids, 4:(100501), 2019.
  33. Turbulence modeling in the age of data. Annu. Rev. Fluid Mech., 51:357–377, 2019.
  34. Sub-grid scale model classification and blending through deep learning. J. Fluid Mech., 870:784–812, 2019.
  35. Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. J. Fluid Mech., 807:155–166, 2016.
  36. Automating turbulence modelling by multi-agent reinforcement learning. Nat. Mach. Intell., 3(1):87–96, 2021.
  37. H. J. Bae and P. Koumoutsakos. Scientific multi-agent reinforcement learning for wall-models of turbulent flows. Nat. Commun., 13(1):1–9, 2022.
  38. S. Lee and D. You. Data-driven prediction of unsteady flow fields over a circular cylinder using deep learning. J. Fluid Mech., 879:217–254, 2019.
  39. An empirical mean-field model of symmetry-breaking in a turbulent wake. Sci. Adv., 8(19):eabm4786, 2022.
  40. O. San and R. Maulik. Neural network closures for nonlinear model order reduction. Adv. Comput. Math., 44(6):1717–1750, 2018.
  41. Sparse identification of nonlinear dynamics with low-dimensionalized flow representations. J. Fluid Mech., 926:A10, 2021.
  42. Predictions of turbulent shear flows using deep neural networks. Phys. Rev. Fluids, 4:054603, 2019.
  43. Robust principal component analysis for modal decomposition of corrupt fluid flows. Phys. Rev. Fluids, 5:054401, 2020.
  44. Data-driven sparse sensor placement for reconstruction: Demonstrating the benefits of exploiting known patterns. IEEE Control Systems Magazine, 38(3):63–86, 2018.
  45. Assessment of supervised machine learning for fluid flows. Theor. Comput. Fluid Dyn., 34(4):497–519, 2020.
  46. Interpretable deep learning for prediction of prandtl number effect in turbulent heat transfer. J. Fluid Mech., 955:A14, 2023.
  47. Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control. J. Fluid Mech,, 865:281–302, 2019.
  48. Deep model predictive flow control with limited sensor data and online learning. Theor. Comput. Fluid Dyn., 34(4):577–591, 2020.
  49. Artificial intelligence control of a turbulent jet. J. Fluid Mech., 897:A27, 2020.
  50. Robust flow control and optimal sensor placement using deep reinforcement learning. J. Fluid Mech., 913:A25, 2021.
  51. J. Park and H. Choi. Machine-learning-based feedback control for drag reduction in a turbulent channel flow. J. Fluid Mech., 904:A24, 2020.
  52. Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows. Phys. Rev. Fluids, 6(5):053902, 2021.
  53. Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning. Nat. Mach. Intell., 3(11):945–951, 2021.
  54. Super-resolution generative adversarial networks of randomly-seeded fields. Nat. Mach. Intell., 4:1165–1173, 2022.
  55. L. Sun and J.-X. Wang. Physics-constrained Bayesian neural network for fluid flow reconstruction with sparse and noisy data. Theor. Appl. Mech. Lett., 10(3):161–169, 2020.
  56. Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels. Phys. Fluids, 33(7):073603, 2021.
  57. Super-resolution and denoising of 4D-flow MRI using physics-informed deep neural nets. Comput. Methods Programs Biomed., 197:105729, 2020.
  58. A. Vlasenko and C. Schnörr. Superresolution and denoising of 3D fluid flow estimates. In Joint Pattern Recognition Symposium, pages 482–491. Springer, 2009.
  59. A. Pradhan and K. Duraisamy. Variational multi-scale super-resolution: A data-driven approach for reconstruction and predictive modeling of unresolved physics. arXiv:2101.09839, 2021.
  60. Using physics-informed enhanced super-resolution generative adversarial networks for subfilter modeling in turbulent reactive flows. Proc. Combust. Inst., 38(2):2617–2625, 2021.
  61. tempogan: A temporally coherent, volumetric GAN for super-resolution fluid flow. ACM Trans. Graph., 37(4):1–15, 2018.
  62. Super-resolution reconstruction of turbulent flows with machine learning. J. Fluid Mech., 870:106–120, 2019.
  63. Shallow neural networks for fluid flow reconstruction with limited sensors. Proc. Roy. Soc. A, 476(2238):20200097, 2020.
  64. Deep learning at scale for subgrid modeling in turbulent flows: regression and reconstruction. In International Conference on High Performance Computing, pages 541–560. Springer, 2019.
  65. SURFNet: Super-resolution of turbulent flows with transfer learning using small datasets. In IEEE 30th International Conference on Parallel Architectures and Compilation Techniques (PACT), pages 331–344. IEEE, 2021.
  66. Deep learning methods for super-resolution reconstruction of turbulent flows. Phys. Fluids, 32(2):025105, 2020.
  67. Unsupervised deep learning for super-resolution reconstruction of turbulence. J. Fluid Mech., 910:A29, 2021.
  68. Neural network–based pore flow field prediction in porous media using super resolution. Phys. Rev. Fluids, 7(7):074302, 2022.
  69. From coarse wall measurements to turbulent velocity fields through deep learning. Phys. Fluids, 33(7):075121, 2021.
  70. High-fidelity reconstruction of turbulent flow from spatially limited data using enhanced super-resolution generative adversarial network. Phys. Fluids, 33(12):125119, 2021.
  71. N. J. Nair and A. Goza. Leveraging reduced-order models for state estimation using deep learning. J. Fluid Mech., 897:R1, 2020.
  72. Learning representations by back-propagation errors. Nature, 322:533—536, 1986.
  73. D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv:1412.6980, 2014.
  74. Data-driven sensor placement with shallow decoder networks. arXiv:2202.05330, 2022.
  75. P. Domingos. A few useful things to know about machine learning. Commun. ACM, 55(10):78–87, 2012.
  76. Gradient-based learning applied to document recognition. Proc. IEEE, 86(11):2278–2324, 1998.
  77. Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low dimensionalization. Theor. Comput. Fluid Dyn., 35(5):633–658, 2021.
  78. T. Nakamura and K. Fukagata. Robust training approach of neural networks for fluid flow state estimations. Int. J. Heat Fluid Flow, 96:108997, 2022.
  79. Deep hierarchical super resolution for scientific data. IEEE Trans. Vis. Comput. Graph., 2022.
  80. RAISR: rapid and accurate image super resolution. IEEE Trans. Comput. Imaging, 3(1):110–125, 2016.
  81. Generative adversarial networks. Commun. ACM, 63(11):139–144, 2020.
  82. Unsupervised machine-learning-based sub-grid scale modeling for coarse-grid LES. In review, 2023.
  83. D. E. Rumelhart and D. Zipser. Feature discovery by competitive learning. Cogn. Sci., 9(1):75–112, 1985.
  84. Artificial neural networks for solving ordinary and partial differential equations. IEEE Trans. Neural Netw., 9(5):987–1000, 1998.
  85. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys., 378:686–707, 2019.
  86. Physics-informed machine learning. Nat. Rev. Phys., 3(6):422–440, 2021.
  87. Physics-informed neural networks (PINNs) for fluid mechanics: A review. Acta Mech. Sin., pages 1–12, 2022.
  88. X. Zhu and A. B. Goldberg. Introduction to semi-supervised learning, volume 3. Morgan & Claypool Publishers, 2009.
  89. Super-resolution analysis with machine learning for low-resolution flow data. In 11th International Symposium on Turbulence and Shear Flow Phenomena (TSFP11), Southampton, UK, number 208, 2019.
  90. Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows. J. Fluid Mech., 909(A9), 2021.
  91. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016.
  92. S. J. Pan and Q. Yang. A survey on transfer learning. IEEE Trans. Knowl. Data Eng., 22(10):1345–1359, 2009.
  93. Convolutional-network models to predict wall-bounded turbulence from wall quantities. J. Fluid Mech., 928:A27, 2021.
  94. Multiresolution convolutional autoencoders. J. Comput. Phys., page 111801, 2022.
  95. P. Pant and A. B. Farimani. Deep learning for efficient reconstruction of high-resolution turbulent DNS data. arXiv:2010.11348, 2020.
  96. Deep learning methods for super-resolution reconstruction of temperature fields in a supersonic combustor. AIP Adv., 10(11):115021, 2020.
  97. Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance. arXiv:2103.09020, 2021.
  98. From here to there: Video inbetweening using 3D convolutions. arXiv:1905.10240, 2019.
  99. A. Shrivastava R. Arora. Spatio-temporal super-resolution of dynamical systems using physics-informed deep-learning. AAAI 2023: Workshop on AI to Accelerate Science and Engineering (AI2ASE), 2022.
  100. J. Kim and C. Lee. Prediction of turbulent heat transfer using convolutional neural networks. J. Fluid Mech., 882:A18, 2020.
  101. Generalization techniques of neural networks for fluid flow estimation. Neural Comput. App., 34(5):3647–3669, 2022.
  102. Super-resolution simulation for real-time prediction of urban micrometeorology. SOLA, 15:178–182, 2019.
  103. Super-resolution of near-surface temperature utilizing physical quantities for real-time prediction of urban micrometeorology. Build. Environ., 209:108597, 2022.
  104. Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141, 2018.
  105. S. Discetti and Y. Liu. Machine learning for flow field measurements: a perspective. Meas. Sci. Technol., 34:021001, 2023.
  106. Super-resolution reconstruction of turbulent velocity fields using a generative adversarial network-based artificial intelligence framework. Phys. Fluids, 31:125111, 2019.
  107. Predicting the near-wall velocity of wall turbulence using a neural network for particle image velocimetry. Phys. Fluids, 32(11):115105, 2020.
  108. Nonlinear mode decomposition with convolutional neural networks for fluid dynamics. J. Fluid Mech., 882:A13, 2020.
  109. R. J. Adrian. Twenty years of particle image velocimetry. Exp. Fluids, 39(2):159–169, 2005.
  110. Dense motion estimation of particle images via a convolutional neural network. Exp. Fluids, 60:60–73, 2019.
  111. Discriminative unsupervised feature learning with exemplar convolutional neural networks. IEEE Trans. Pattern Anal. Mach. Intell., 38, 2019.
  112. Developing particle image velocimetry software based on a deep neural network. J. Flow Vis. Image Process., 27(4), 2020.
  113. Experimental velocity data estimation for imperfect particle images using machine learning. Phys. Fluids, 33(8):087121, 2021.
  114. K. Fukami and K. Taira. Learning the nonlinear manifold of extreme aerodynamics. NeurIPS2022, 2022.
  115. Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data. Phys. Fluids, 32:095110, 2020.
  116. Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows. Expert Syst. Appl., 202:117038, 2022.
  117. Model order reduction with neural networks: Application to laminar and turbulent flows. SN Comput. Sci., 2:467, 2021.
  118. Deep learning to discover and predict dynamics on an inertial manifold. Phys. Rev. E, 101(6):062209, 2020.
  119. K. Fukami and K. Taira. Grasping extreme aerodynamics on a low-dimensional manifold. arXiv:2305.08024, 2023.
  120. A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst., 32(1):4–24, 2020.
  121. Data-driven sparse reconstruction of flow over a stalled aerofoil using experimental data. Data-Centric Eng., 2, 2021.
  122. A. Giannopoulos and J.-L. Aider. Data-driven order reduction and velocity field reconstruction using neural networks: The case of a turbulent boundary layer. Phys. Fluids, 32(9):095117, 2020.
  123. Probabilistic neural networks for fluid flow surrogate modeling and data recovery. Phys. Rev. Fluids, 5:104401, 2020.
  124. R. Everson and L. Sirovich. Karhunen–Loeve procedure for gappy data. J. Opt. Soc. Am., 12(8):1657–1664, 1995.
  125. Aerodynamic data reconstruction and inverse design using proper orthogonal decomposition. AIAA J., 42(8):1505–1516, 2004.
  126. R. J. Adrian and P. Moin. Stochastic estimation of organized turbulent structure: homogeneous shear flow. J. Fluid Mech., 190:531–559, 1988.
  127. Sparse sensor-based cylinder flow estimation using artificial neural networks. Phys. Rev. Fluids, 7(2):024707, 2022.
  128. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Comput., 9:1735–1780, 1997.
  129. Machine learning for fluid flow reconstruction from limited measurements. J. Comput. Phys., 448:110733, 2022.
  130. J. L. Lumley. The structure of inhomogeneous turbulent flows. In A. M. Yaglom and V. I. Tatarski, editors, Atmospheric turbulence and radio wave propagation. Nauka, 1967.
  131. Turbulence, Coherent Structures, Dynamical Systems and Symmetry. Cambridge Univ. Press, 2nd edition, 2012.
  132. Modal analysis of fluid flows: An overview. AIAA J., 55(12):4013–4041, 2017.
  133. Reducing the dimensionality of data with neural networks. Science, 313(5786):504–507, 2006.
  134. Stochastic backpropagation and approximate inference in deep generative models. In International conference on machine learning, pages 1278–1286. PMLR, 2014.
  135. A. J. Smola and B. Schölkopf. A tutorial on support vector regression. Stat. Comput., 14(3):199–222, 2004.
  136. J. H. Friedman. Greedy function approximation: a gradient boosting machine. Ann. Stat., pages 1189–1232, 2001.
  137. Discovering governing equations from data by sparse identification of nonlinear dynamical systems. Proc. Natl. Acad. Sci. U.S.A., 113(15):3932–3937, 2016.
  138. Robust flow reconstruction from limited measurements via sparse representation. Phys. Rev. Fluids, 4(10):103907, 2019.
  139. Assessments of epistemic uncertainty using gaussian stochastic weight averaging for fluid-flow regression. Phys. D: Nonlinear Phenom., 440:133454, 2022.
  140. Machine-learning-based reconstruction of transient vortex-airfoil wake interaction. AIAA paper, 2022-3244, 2022.
  141. Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learning. Theor. Comput. Fluid Dyn., 2023.
  142. Predicting drag on rough surfaces by transfer learning of empirical correlations. J. Fluid Mech., 933:A18, 2022.
  143. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science, 367(6481):1026–1030, 2020.
  144. A deep-learning approach for reconstructing 3D turbulent flows from 2D observation data. arXiv:2208.05754, 2022.
  145. ESRGAN: Enhanced super-resolution generative adversarial networks. In Proceedings of the European conference on computer vision (ECCV) workshops, pages 63–79, 2018.
  146. Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes. Theor. Comput. Fluid Dyn., 34(4):367–388, 2020.
  147. CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers. Fluid Dyn. Res., 52(6):065501, 2020.
  148. MeshfreeFlowNet: A physics-constrained deep continuous space-time super-resolution framework. In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1–15. IEEE, 2020.
  149. TransFlowNet: A physics-constrained transformer framework for spatio-temporal super-resolution of flow simulations. J. Comput. Sci., 65(101906), 2022.
  150. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223–2232, 2017.
  151. Meta-learning PINN loss functions. J. Comput. Phys., 458:111121, 2022.
  152. Towards real-time prediction of velocity field around a building using generative adversarial networks based on the surface pressure from sparse sensor networks. J. Wind. Eng. Ind., 231:105243, 2022.
  153. S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448–456, 2015.
  154. Deep hierarchical super-resolution for scientific data reduction and visualization. arXiv:2107.00462, 2021.
  155. DNS-based predictive control of turbulence: an optimal benchmark for feedback algorithms. J. Fluid Mech., 447:179–225, 2001.
  156. State estimation in wall-bounded flow systems. Part 2. Turbulent flows. J. Fluid Mech., 552:167–187, 2006.
  157. State estimation in wall-bounded flow systems. Part 3. The ensemble kalman filter. J. Fluid Mech., 682:289–303, 2011.
  158. T. Suzuki and Y. Hasegawa. Estimation of turbulent channel flow at R⁢eτ=100𝑅subscript𝑒𝜏100{Re}_{\tau}=100italic_R italic_e start_POSTSUBSCRIPT italic_τ end_POSTSUBSCRIPT = 100 based on the wall measurement using a simple sequential approach. J. Fluid Mech., 830:760–796, 2006.
  159. Super-resolution reconstruction of turbulent flow fields at various Reynolds numbers based on generative adversarial networks. Phys. Fluids, 34(1):015130, 2022.
  160. Data-driven three-dimensional super-resolution imaging of a turbulent jet flame using a generative adversarial network. Appl. Opt., 59(19):5729–5736, 2020.
  161. Adversarial sampling of unknown and high-dimensional conditional distributions. J. Comput. Phys., 450:110853, 2022.
  162. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4681–4690, 2017.
  163. Adversarial super-resolution of climatological wind and solar data. Proc. Natl. Acad. Sci. U.S.A., 117(29):16805–16815, 2020.
  164. Diversity-sensitive conditional generative adversarial networks. In 7th International Conference on Learning Representations, ICLR 2019. International Conference on Learning Representations, ICLR, 2019.
  165. Network structure of two-dimensional decaying isotropic turbulence. J. Fluid Mech., 795:R2, 2016.
  166. Single image super-resolution based on multi-scale competitive convolutional neural network. Sensors, 789(18):1–17, 2018.
  167. V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proc. 27th International Conference on Machine Learning, 2010.
  168. Physics-informed deep super-resolution for spatiotemporal data. arXiv:2208.01462, 2022.
  169. A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries. Phys. Fluids, 33(2):027104, 2021.
  170. PointNet: Deep learning on point sets for 3D classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 652–660, 2017.
  171. Fluid simulation system based on graph neural network. arXiv:2202.12619, 2022.
  172. A comparison of neural network architectures for data-driven reduced-order modeling. Comput. Methods Appl. Mech. Eng., 393:114764, 2022.
  173. PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state pdes on irregular domain. J. Comput. Phys., page 110079, 2020.
  174. T. Kajishima and K. Taira. Computational Fluid Dynamics: Incompressible Turbulent Flows. Springer, 2017.
  175. Machine learning–accelerated computational fluid dynamics. Proc. Natl. Acad. Sci. U.S.A., 118(21):e2101784118, 2021.
  176. An approximate deconvolution model for large-eddy simulation with application to incompressible wall-bounded flows. Phys. Fluids, 13(4):997–1015, 2001.
  177. State estimation in minimal turbulent channel flow: A comparative study of 4DVar and PINN. Int. J. Heat Fluid Flow, 99:109073, 2023.
  178. Synchronization to big data: Nudging the Navier-Stokes equations for data assimilation of turbulent flows. Phys. Rev. X, 10(1):011023, 2020.
  179. Inferring flow parameters and turbulent configuration with physics-informed data assimilation and spectral nudging. Phys. Rev. Fluids, 3(10):104604, 2018.
  180. Y. Yasuda and R. Onishi. Spatio-temporal super-resolution data assimilation (SRDA) utilizing deep neural networks with domain generalization technique toward four-dimensional SRDA. arXiv:2212.03656, 2022.
  181. Physics-guided deep learning for generating turbulent inflow conditions. J. Fluid Mech., 936:A21, 2022.
  182. Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions. Sci. Rep., 12(3726), 2022.
  183. Machine-learning-based reconstruction of turbulent vortices from sparse pressure sensors in a pump sump. J. Fluids Eng., 144(12):121501, 2022.
  184. A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence. J. Turb, (9):N31, 2008.
  185. X. Wu and P. Moin. A direct numerical simulation study on the mean velocity characteristics in turbulent pipe flow. J. Fluid Mech., 608:81–112, 2008.
  186. A database for reduced-complexity modeling of fluid flows. AIAA J., 2023.
Citations (79)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up