Funder
United States Department of Defense | United States Navy | Office of Naval Research
United States Department of Defense | United States Air Force | AFMC | Air Force Office of Scientific Research
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software
Reference63 articles.
1. N. Watters et al. Visual interaction networks: Learning a physics simulator from video. In Advances in Neural Information Processing Systems (eds Garnett, R. et al.) Vol. 30 (Curran Associates, 2017); https://proceedings.neurips.cc/paper/2017/file/8cbd005a556ccd4211ce43f309bc0eac-Paper.pdf
2. Gonzalez, F. J. & Balajewicz, M. Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems. Preprint at https://arxiv.org/abs/1808.01346 (2018).
3. Vlachas, P. R., Byeon, W., Wan, Z. Y., Sapsis, T. P. & Koumoutsakos, P. Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks. Proc. R. Soc. A 474, 20170844 (2018).
4. Champion, K., Lusch, B., Kutz, J. N. & Brunton, S. L. Data-driven discovery of coordinates and governing equations. Proc. Natl Acad. Sci. USA 116, 22445–22451 (2019).
5. Carlberg, K. T. et al. Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning. J. Comput. Phys. 395, 105–124 (2019).
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