Phase Space Learning with Neural Networks

Author:

López García Jaime,Rivero Ángel

Publisher

Springer International Publishing

Reference23 articles.

1. Kim, B., Azevedo, V.C., Thuerey, N., Kim, T., Gross, M., Solenthaler, B.: Deep fluids: a generative network for parameterized fluid simulations. Comput. Graph. Forum 38, 59–70 (2019). Wiley Online Library

2. Wiewel, S., Becher, M., Thuerey, N.: Latent space physics: towards learning the temporal evolution of fluid flow. Comput. Graph. Forum 38, 71–82 (2019). Wiley Online Library

3. Raissi, M., Perdikaris, P., Karniadakis, G.E.: Multistep neural networks for data-driven discovery of nonlinear dynamical systems (2018). arXiv:1801.01236

4. Funahashi, K.-I., Nakamura, Y.: Approximation of dynamical systems by continuous time recurrent neural networks. Neural Netw. 6(6), 801–806 (1993)

5. Chen, T., Chen, H.: Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems. IEEE Trans. Neural Netw. 6(4), 911–917 (1995)

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