Fetching the paper…
Reading the bibliography…
Spatio-temporal dynamics of physical processes are generally modeled using partial differential equations (PDEs).
Learning representations by back-propagating errors
Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986 · 1986
Earlier work this paper cites.
Learning state space trajectories in recurrent neural networks
Pearlmutter, B.A., 1989 · 1989
Earlier work this paper cites.
Backpropagation through time: what it does and how to do it
Werbos, P.J., 1990 · 1990
Earlier work this paper cites.
Approximation of dynamical systems by continuous time recurrent neural networks
Funahashi, K.i., Nakamura, Y., 1993 · 1993
Earlier work this paper cites.
An application of recurrent nets to phone probability estimation
Robinson, T., 1994 · 1994
Earlier work this paper cites.
Long short-term memory
Hochreiter, S., Schmidhuber, J., 1997 · 1997
Earlier work this paper cites.
Bidirectional recurrent neural networks
Schuster, M., Paliwal, K.K., 1997 · 1997
Earlier work this paper cites.
Universal differential equations for scientific machine learning
Rackauckasa, C., Mac, Y., Martensend, J., Warnera, C., Zubove, K., Supekara, R., Skinnera, D., Ramadhana, A., Edelmana, A., 2020 · 2001
Earlier work this paper cites.
Approximation of dynamical time-variant systems by continuous-time recurrent neural networks
Li, X.D., Ho, J.K., Chow, T.W., 2005 · 2005
Earlier work this paper cites.
Partial differential equations: An introduction
Strauss, W.A., 2007 · 2007
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., Bengio, Y., 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., Le, Q., 2014 · 2014
Cited alongside, same era.
Convolutional lstm network: A machine learning approach for precipitation nowcasting, in: Advances in neural information processing systems, pp. 802–810
Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c., 2015 · 2015
Cited alongside, same era.
Unsupervised learning for physical interaction through video prediction, in: Advances in neural information processing systems, pp. 64–72
Finn, C., Goodfellow, I., Levine, S., 2016 · 2016
Cited alongside, same era.
Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Cited alongside, same era.
Ask me anything: Dynamic memory networks for natural language processing, in: International conference on machine learning, pp. 1378–1387
Kumar, A., Irsoy, O., Ondruska, P., Iyyer, M., Bradbury, J., Gulrajani, I., Zhong, V., Paulus, R., Socher, R., 2016 · 2016
Learning partial differential equations via data discovery and sparse optimization
Schaeffer, H., 2017 · 2017
Later among the works it cites.
A proposal on machine learning via dynamical systems
Weinan, E., 2017 · 2017
Later among the works it cites.
Reversible architectures for arbitrarily deep residual neural networks, in: Thirty-Second AAAI Conference on Artificial Intelligence
Chang, B., Meng, L., Haber, E., Ruthotto, L., Begert, D., Holtham, E., 2018 · 2018
Later among the works it cites.
Neural ordinary differential equations, in: Advances in neural information processing systems, pp. 6571–6583
Chen, T.Q., Rubanova, Y., Bettencourt, J., Duvenaud, D.K., 2018 · 2018
Later among the works it cites.
Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations, in: International Conference on Machine Learning, pp. 3282–3291
Lu, Y., Zhong, A., Li, Q., Dong, B., 2018 · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Bridging the gaps between residual learning, recurrent neural networks and visual cortex
Liao, Q., Poggio, T., 2016 · 2016
Cited alongside, same era.
Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections, in: Advances in neural information processing systems, pp. 2802–2810
Mao, X., Shen, C., Yang, Y.B., 2016 · 2016
Cited alongside, same era.
Synthesis of recurrent neural networks for dynamical system simulation
Trischler, A.P., D’Eleuterio, G.M., 2016 · 2016
Cited alongside, same era.
Desire: Distant future prediction in dynamic scenes with interacting agents, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 336–345
Lee, N., Choi, W., Vernaza, P., Choy, C.B., Torr, P.H., Chandraker, M., 2017 · 2017
Cited alongside, same era.
Physics informed deep learning (part ii): Data-driven discovery of nonlinear partial differential equations
Raissi, M., Perdikaris, P., Karniadakis, G.E., 2017 · 2017
Cited alongside, same era.
Data-driven discovery of partial differential equations
Rudy, S.H., Brunton, S.L., Proctor, J.L., Kutz, J.N., 2017 · 2017
Cited alongside, same era.
Hybridnet: Integrating model-based and data-driven learning to predict evolution of dynamical systems, in: Conference on Robot Learning, pp. 551–560
Long, Y., She, X., Mukhopadhyay, S., 2018a
Cited in the paper.
Deep hidden physics models: Deep learning of nonlinear partial differential equations
Raissi, M., 2018 · 2018
Later among the works it cites.
Hidden physics models: Machine learning of nonlinear partial differential equations
Raissi, M., Karniadakis, G.E., 2018 · 2018
Later among the works it cites.
Deep learning for physical processes: Incorporating prior scientific knowledge
de Bezenac, E., Pajot, A., Gallinari, P., 2019 · 2019
Later among the works it cites.
Pde-net 2.0: Learning pdes from data with a numeric-symbolic hybrid deep network
Long, Z., Lu, Y., Dong, B., 2019 · 2019
Later among the works it cites.
Unsupervised learning of object structure and dynamics from videos, in: Advances in Neural Information Processing Systems, pp. 92–102
Minderer, M., Sun, C., Villegas, R., Cole, F., Murphy, K.P., Lee, H., 2019 · 2019
Later among the works it cites.
Deep learning algorithm for data-driven simulation of noisy dynamical system
Yeo, K., Melnyk, I., 2019 · 2019
Later among the works it cites.