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Simulating complex physical systems often involves solving partial differential equations (PDEs) with some closures due to the presence of multi-scale physics that cannot be fully resolved.
doi:10.1080/14786441608635602
L. Rayleigh, LIX. On convection currents in a horizontal layer of fluid, when the higher temperature is on the under side, Philosophical Magazine Series 6 32 (192) (1916) 529–546 · 1916
Earlier work this paper cites.
J. Weickert, H. Hagen, Visualization and processing of tensor fields, Springer Science & Business Media, 2005
2005
Earlier work this paper cites.
doi:10.1103/PhysRevLett.102.064501
H. Johnston, C. R. Doering, A comparison of turbulent thermal convection between conditions of constant temperature and constant heat flux boundaries, Physical Review Letters 102 (6) (2009) 064501 · 2009
Earlier work this paper cites.
doi:10.1088/1367-2630/12/7/075022
O. Shishkina, R. J. A. M. Stevens, S. Grossmann, D. Lohse, Boundary layer structure in turbulent thermal convection and its consequences for the required numerical resolution, New Journal of Physics 12 (7) (2010) 075022 · 2010
Earlier work this paper cites.
A. Cherian, S. Sra, A. Banerjee, N. Papanikolopoulos, Efficient similarity search for covariance matrices via the jensen-bregman logdet divergence, in: Computer Vision (ICCV), 2011 IEEE International Conference on, IEEE, 2011, pp. 2399–2406
2011
Earlier work this paper cites.
doi:10.1016/j.camwa.2012.07.001
J. Wang, D. Wang, P. Lallemand, L.-S. S. Luo, Lattice Boltzmann simulations of thermal convective flows in two dimensions, Computers and Mathematics with Applications 65 (2) (2013) 262–286 · 2012
Earlier work this paper cites.
doi:10.1017/jfm.2013.488
E. P. van der Poel, R. J. A. M. Stevens, D. Lohse, Comparison between two- and three-dimensional Rayleigh–Bénard convection, Journal of Fluid Mechanics 736 (2013) 177–194 · 2013
Earlier work this paper cites.
D. J. Gagne, A. McGovern, M. Xue, Machine learning enhancement of storm-scale ensemble probabilistic quantitative precipitation forecasts, Weather and Forecasting 29 (4) (2014) 1024–1043
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative Adversarial Nets, in: Advances in neural information processing systems, 2014, pp. 2672–2680
2014
Earlier work this paper cites.
Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (7553) (2015) 436
2015
Earlier work this paper cites.
J. Ling, A. Kurzawski, J. Templeton, Reynolds averaged turbulence modelling using deep neural networks with embedded invariance, Journal of Fluid Mechanics 807 (2016) 155–166
2016
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, Improved techniques for training GANs, in: Advances in Neural Information Processing Systems, 2016, pp. 2234–2242
2016
Cited alongside, same era.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al., Tensorflow: A system for large-scale machine learning., in: OSDI, Vol. 16, 2016, pp. 265–283
2016
Cited alongside, same era.
J.-X. Wang, J.-L. Wu, H. Xiao, Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data, Physical Review Fluids 2 (3) (2017) 034603
2017
Cited alongside, same era.
E. Racah, C. Beckham, T. Maharaj, S. E. Kahou, M. Prabhat, C. Pal, Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events, in: Advances in Neural Information Processing Systems, 2017, pp. 3402–3413
2017
Cited alongside, same era.
S. Rasp, M. S. Pritchard, P. Gentine, Deep learning to represent subgrid processes in climate models, Proceedings of the National Academy of Sciences 115 (39) (2018) 9684–9689
2018
Later among the works it cites.
P. D. Dueben, P. Bauer, Challenges and design choices for global weather and climate models based on machine learning, Geoscientific Model Development 11 (10) (2018) 3999–4009
2018
Later among the works it cites.
B. Lusch, J. N. Kutz, S. L. Brunton, Deep learning for universal linear embeddings of nonlinear dynamics, Nature communications 9 (1) (2018) 4950
2018
Later among the works it cites.
2018
Later among the works it cites.
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M. Prabhat, J. Biard, S. Ganguly, S. Ames, K. Kashinath, S. Kim, S. Kahou, T. Maharaj, C. Beckham, T. O’Brien, et al., Climatenet: A machine learning dataset for climate science research, in: AGU Fall Meeting Abstracts, 2017
2017
Cited alongside, same era.
A. Karpatne, G. Atluri, J. H. Faghmous, M. Steinbach, A. Banerjee, A. Ganguly, S. Shekhar, N. Samatova, V. Kumar, Theory-guided data science: A new paradigm for scientific discovery from data, IEEE Transactions on Knowledge and Data Engineering 29 (10) (2017) 2318–2331
2017
Cited alongside, same era.
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, D. Krishnan, Unsupervised pixel-level domain adaptation with generative adversarial networks, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 1, 2017, p. 7
2017
Cited alongside, same era.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A. C. Courville, Improved training of Wasserstein GANs, in: Advances in Neural Information Processing Systems, 2017, pp. 5767–5777
2017
Cited alongside, same era.
J.-L. Wu, H. Xiao, E. Paterson, Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework, Physical Review Fluids 3 (7) (2018) 074602
2018
Cited alongside, same era.
N. D. Brenowitz, C. S. Bretherton, Prognostic validation of a neural network unified physics parameterization, Geophysical Research Letters 45 (12) (2018) 6289–6298
2018
Cited alongside, same era.
T. Schneider, S. Lan, A. Stuart, J. Teixeira, Earth system modeling 2.0: A blueprint for models that learn from observations and targeted high-resolution simulations, Geophysical Research Letters 44 (24)
Cited in the paper.
Cited in the paper.
Y. Lu, A. Zhong, Q. Li, B. Dong, Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations , in: J. Dy, A. Krause (Eds.), Proceedings of the 35th International Conference on Machine Learning, Vol. 80 of Proceedings of Machine Learning Research, PMLR, Stockholmsmässan, Stockholm Sweden, 2018, pp. 3282–3291. URL http://proceedings.mlr.press/v80/lu18d.html
2018
Later among the works it cites.
Z. Long, Y. Lu, X. Ma, B. Dong, PDE-net: Learning PDEs from data, in: Proceedings of the 35th International Conference on Machine Learning (ICML 2018), 2018
2018
Later among the works it cites.
D. B. Chirila, Towards Lattice Boltzmann models for climate sciences: The GeLB programming language with applications , Ph.D. thesis, University of Bremen (2018). URL https://elib.suub.uni-bremen.de/peid/D00106468.html
2018
Later among the works it cites.
M. Raissi, P. Perdikaris, G. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational Physics 378 (2019) 686–707
2019
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Y. Zeng, J.-L. Wu, H. Xiao, Enforcing physical constraints on generative adversarial networks with application to fluid flows, in preparation (2019)
2019
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C. Michelen, J.-L. Wu, H. Xiao, Constructing pde-informed covariance for quantification of model uncertaintie, in preparation (2019)
2019
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