![]() In: International Conference on Machine Learning, pp. Prentice Hall, Upper Saddle River (1998)īergstra, J., Yamins, D., Cox, D.: Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. Haykin, S.: Neural Networks: A Comprehensive Foundation. In: Proceedings of 27th International Conference on Machine Learning (2010) Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. Kingma, D., Ba, J.: A method for stochastic optimization (2014). ![]() Yu, J., Hesthaven, J.S.: Flowfield reconstruction method using artificial neural network. Maulik, R., San, O., Rasheed, A., Vedula, P.: Sub-grid modelling for two-dimensional turbulence using neural networks. Nature 322, 533–536 (1986)ĭomingos, P.: A few useful things to know about machine learning. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagation errors. Cambridge University Press, Cambridge (2019) 865, 281–302 (2019)īrunton, S.L., Kutz, J.N.: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Rabault, J., Kuchta, M., Jensen, A., Reglade, U., Cerardi, N.: Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control. Fluids 31, 075108 (2019)Įrichson, N.B., Mathelin, L., Yao, Z., Brunton, S.L., Mahoney, M.W., Kutz, J.N.: Shallow learning for fluid flow reconstruction with limited sensors and limited data (2019) arXiv:1902.07358ĭuriez, T., Brunton, S.L., Noack, B.R.: Machine learning control-taming nonlinear dynamics and turbulence. Fluids 4, 064603 (2019)ĭeng, Z., Chen, Y., Liu, Y., Kim, K.C.: Time-resolved turbulent velocity field reconstruction using a long short-term memory (LSTM)-based artificial intelligence framework. 870, 106–120 (2019)įukami, K., Nabae, Y., Kawai, K., Fukagata, K.: Synthetic turbulent inflow generator using machine learning. Fluids 3, 104604 (2018)įukami, K., Fukagata, K., Taira, K.: Super-resolution reconstruction of turbulent flows with machine learning. Leoni, P.C.D., Mazzino, A., Biferale, L.: Inferring flow parameters and turbulent configuration with physics-informed data assimilation and spectral nudging. Salehipour, H., Peltier, W.R.: Deep learning of mixing by two ‘atoms’ of stratified turbulence. Murata, T., Fukami, K., Fukagata, K.: Nonlinear mode decomposition with convolutional neural networks for fluid dynamics. Srinivasan, P.A., Guastoni, L., Azizpour, H., Schlatter, P., Vinuesa, R.: Predictions of turbulent shear flows using deep neural networks. ![]() Lui, H.F.S., Wolf, W.R.: Construction of reduced-order models for fluid flows using deep feedforward neural networks. San, O., Maulik, R.: Extreme learning machine for reduced order modeling of turbulent geophysical flows. Maulik, R., San, O.: A neural network approach for the blind deconvolution of turbulent flows. Gamahara, M., Hattori, Y.: Searching for turbulence models by artificial neural network. 807, 155 (2016)ĭuraisamy, K., Iaccarino, G., Xiao, H.: Turbulence modeling in the age of data. Ling, J., Kurzawski, A., Templeton, J.: Reynolds averaged turbulence modelling using deep neural networks with embedded invariance. (eds.) Constraint Programming and Decision Making: Theory and Applications, pp. 2, 303–314 (1989)īaral, C., Fuentes, O., Kreinovich, V.: Why deep neural networks: a possible theoretical explanation. 4, 251–257 (1991)Ĭybenko, G.: Approximation by superpositions of a sigmoidal function. Hornik, K.: Approximation capabilities of multilayer feedforward networks. Kreinovich, V.Y.: Arbitrary nonlinearity is sufficient to represent all functions by neural networks: a theorem. 52, 477–508 (2020)īrenner, M.P., Eldredge, J.D., Freund, J.B.: Perspective on machine learning for advancing fluid mechanics. 814, 1–4 (2017)īrunton, S.L., Noack, B.R., Koumoutsakos, P.: Machine learning for fluid mechanics. Kutz, J.N.: Deep learning in fluid dynamics.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |