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Turbulence: Machine Learning Methods for Turbulence Modeling I

ORAL · A21 · ID: 682555

Presentations

  • Capturing small scale dynamics of turbulent velocity and scalar fields using deep learning

    ORAL

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    Presenters

    • Dhawal Buaria

      New York University (NYU)

    Authors

    • Dhawal Buaria

      New York University (NYU)

    • Katepalli R Sreenivasan

      New York U., New York University (NYU), NYU, New York, USA, Tandon School of Engineering, Courant Institute of Mathematical Sciences, Department of Physics, New York University, New York, New York University, Department of Mechanical and Aerospace Engineering, Department of Physics and the Courant Institute of Mathematical Sciences, New York University, New York, USA

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  • Lagrangian Large Eddy Simulations via Physics-Informed Machine Learning

    ORAL

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    Publication: Tian, Yifeng, et al. "Lagrangian Large Eddy Simulations via Physics Informed Machine Learning." arXiv preprint arXiv:2207.04012 (2022).

    Presenters

    • Michael Chertkov

      University of Arizona

    Authors

    • Michael Chertkov

      University of Arizona

    • Yifeng Tian

      Los Alamos National Laboratory

    • Mikhail Stepanov

      University of Arizona, The University of Arizona

    • Chris L Fryer

      Los Alamos Natl Lab, Los Alamos National Laboratory

    • Michael Woodward

      University of Arizona

    • Criston M Hyett

      The University of Arizona, University of Arizona

    • Daniel Livescu

      LANL, Los Alamos National Laboratory

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  • Machine Learning assisted modeling of velocity gradient dynamics in turbulent flows

    ORAL

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    Publication: We are planning to submit the extended version of the abstract to the journal of Physics of Fluids

    Presenters

    • Deep Shikha

      Indian Institute of Technology Delhi, INDIA

    Authors

    • Deep Shikha

      Indian Institute of Technology Delhi, INDIA

    • Sawan S Sinha

      Indian Institute of Technology Delhi, INDIA, Indian Institute of Technology Delhi , INDIA

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  • Turbulence Model Development based on a Novel Method Combining Gene Expression Programming and Artificial Neural Network

    ORAL

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    Publication: [1] I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, MIT press, 2016. <br>[2] J. Ling, A. Kurzawski, J. Templeton, Reynolds averaged turbulence modelling using deep neural networks with embedded invariance, J. Fluid Mech. 807 (2016) 155–166. <br>[3] C. Xie, Z. Yuan, J. Wang, Artificial neural network-based nonlinear algebraic models for large eddy simulation of turbulence, Phys. Fluids 32 (11) (2020) 115101. <br>[4] M. Raissi, P. Perdikaris, G. E. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, J. Comput. Phys. 378 (2018) 686–707. <br>[5] X. Yang, S. Zafar, J.-X. Wang, H. Xiao, Predictive large-eddy-simulation wall modeling via physics-informed neural networks, Phys. Rev. Fluids 4 (3) (2019) 034602. <br>[6] Q. Liu, J. Niu, P. Lu, F. Dong, F. Zhou, X. Meng, W. Xu, S. Li, B. X. Hu, Interannual and seasonal variations of permafrost thaw depth on the qinghai-tibetan plateau: A comparative study using long short-term memory, convolutional neural networks, and random forest, Sci. Total Environ. 838 (2022) 155886. <br>[7] S. Sahoo, C. H. Lampert, G. Martius, Learning equations for extrapolation and control, in: International Con ference on Machine Learning, 2018, pp. 4442–4450. <br>[8] S. Kim, P. Y. Lu, S. Mukherjee, M. Gilbert, M. Soljacic, Integration of neural network-based symbolic regression in deep learning for scientific discovery, IEEE Trans. Neural Netw. Learn. Syst. 32 (9) (2020) 4166–4177. <br>[9] S.-M. Udrescu, M. Tegmark, Ai feynman: A physics-inspired method for symbolic regression, Sci. Adv. 6 (16) (2020) eaay2631. <br>[10] B. K. Petersen, M. L. Larma, T. N. Mundhenk, C. P. Santigago, S. K. Kim, J. T. Kim, Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients, in: International Conference on Learning Representations, 2020.<br>[11] L. Biggio, T. Bendinelli, A. Neitz, A. Lucchi, G. Parascandolo, Neural symbolic regression that scales, in: International Conference on Machine Learning, 2021, pp. 936–945. <br>[12] J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press, 1975. <br>[13] J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT press, 1992. <br>[14] M. Schmidt, H. Lipson, Distilling free-form natural laws from experimental data, Sci. 324 (5923) (2009) 81–85. <br>[15] C. Ferreira, Gene expression programming: A new adaptive algorithm for Solving Problems, Complex Syst. 13 (2) (2001) 87–129. <br>[16] J. Weatheritt, R. D. Sandberg, A novel evolutionary algorithm applied to algebraic modifications of the rans stress strain relationship, J. Comput. Phys. 325 (2016) 22–37. <br>[17] H. Li, Y. Zhao, J. Wang, R. D. Sandberg, Data-driven model development for large- eddy simulation of turbu lence using gene- expression programing, Phys. Fluids 33 (2021) 125–127. <br>[18] M. A. Khan, S. A. Memon, F. Farooq, M. F. Javed, F. Aslam, R. Alyousef, Compressive strength of fly-ash based geopolymer concrete by gene expression programming and random forest, Adv. Civ. Eng. 2021 (2021) 6618407. <br>[19] M. A. Khan, A. Zafar, A. Akbar, M. F. Javed, A. H. Mosavi, Application of gene expression programming (gep) for the prediction of compressive strength of geopolymer concrete, Mater. 14 (5) (2021) 1106. <br>[20] M. Cranmer, A. Sanchez Gonzalez, P. Battaglia, R. Xu, K. Cranmer, D. Spergel, S. Ho, Discovering symbolic models from deep learning with inductive biases, in: Advances in Neural Information Processing Systems, 2020, pp. 17429–17442. <br>[21] B. He, Q. Lu, Q. Yang, J. Luo, Z. Wang, Taylor genetic programming for symbolic regression, ArXiv (2022) arXiv:2205.09751. <br>[22] J. E. Dennis, J. J. More, Quasi-newton methods, motivation and theory, Siam Review 19 (1) (1977) 46–89. <br>[23] M. R. Segal, Machine learning benchmarks and random forest regression, Biostat. (2004) 1–14. <br>[24] X. Glorot, A. Bordes, Y. Bengio, Deep sparse rectifier neural networks, in: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 2011, pp. 315–323. <br>[25] J. Duchi, E. Hazan, Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization, J. Mach. Learn. Res. 12 (2011) 2121–2159. <br>[26] C. H. Kautz, P. R. L. Heron, M. E. Loverude, L. C. Mcdermott, Student understanding of the ideal gas law, part i: A macroscopic perspective, Am. J. Phys. 73 (11) (2005) 1055–1063. <br>[27] I. Newton, A. E. Shapiro, The Principia: Mathematical Principles of Natural Philosophy, Univ. of California Press, 1999. <br>[28] S. B. Pope, Turbulent flows, Cambridge university press, 2000. <br>[29] J. Graham, K. Kanov, X. I. A. Yang, M. Lee, C. Meneveau, A web services accessible database of turbulent channel flow and its use for testing a new integral wall model for les, J. Turbul. 17 (2) (2016) 181–215, doi: 10.7281/T10K26QW. <br>[30] E. Perlman, R. Burns, Y. Li, C. Meneveau, Data exploration of turbulence simulations using a database cluster, in: Proceedings of the 2007 ACM/IEEE Conference on Supercomputing, 2007, pp. 1–11. <br>[31] Y. Li, E. Perlman, M. Wan, Y. Yang, C. Meneveau, R. Burns, S. Chen, A. Szalay, G. Eyink, A public turbulence database cluster and applications to study lagrangian evolution of velocity increments in turbulence, J. Turbul. 9 (31) (2008) 1–29. <br>[32] J. Smagorinsky, General circulation experiments with the primitive equations: I. the basic experiment, Mon. Weather Rev. 91 (3) (1963) 99–164. <br>[33] P. Moin, K. Squires, W. Cabot, S. Lee, A dynamic subgrid-scale model for compressible turbulence and scalar transport, Phys. Fluids 3 (11) (1991) 2746–2757. <br>[34] S. Liu, C. Meneveau, J. Katz, On the properties of similarity subgrid-scale models as deduced from measure ments in a turbulent jet, J. Fluid Mech. 275 (1994) 83–119. <br>[35] R. A. Clark, J. H. Ferziger, W. C. Reynolds, Evaluation of subgrid-scale models using an accurately simulated turbulent flow, J. Fluid Mech. 91 (1) (1979) 1–16. <br>[36] M. Buzzicotti, M. Linkmann, H. Aluie, L. Biferale, J. Brasseur, C. Meneveau, Effect of filter type on the statistics of energy transfer between resolved and subfilter scales from a-priori analysis of direct numerical simulations of isotropic turbulence, J. Turbul. 19 (2) (2018) 167–197.

    Presenters

    • Haochen Li

      Peking Univ

    Authors

    • Haochen Li

      Peking Univ

    • Yaomin Zhao

      Peking University, Peking Univ

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