[52] George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang. Physics-informed machine learning. Nature Reviews Physics, 3(6):422-440, 2021.
[53] Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas, and François Fleuret. Transformers are RNNs: Fast autoregressive transformers with linear attention. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 5156-5165. PMLR, 13-18 Jul 2020. URL https://proceedings.mlr.press/v119/katharopoulos20a.html.
[54] Georgios Kissas, Jacob Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, and Paris Perdikaris. Learning operators with coupled attention, 2022.
[55] Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=rkgNKkHtvB.
[56] Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva, Qing Wang, Michael P. Brenner, and Stephan Hoyer. Machine learning-accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences, 118(21), may 2021. doi: 10.1073/pnas.2101784118. URL https://doi.org/10.1073/2Fpnas.2101784118.
[57] Tamara G. Kolda and Brett W. Bader. Tensor decompositions and applications. SIAM Rev., 51(3):455-500, aug 2009. ISSN 0036-1445. doi: 10.1137/07070111X. URL https://doi.org/10.1137/07070111X.
[58] Jean Kossaifi, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, and Anima Anandkumar. Multi-grid tensorized fourier neural operator for high resolution PDEs, 2023. URL https://openreview.net/forum?id=po-oqRst4Xm.
[59] Nikola Kovachki, Samuel Lanthaler, and Siddhartha Mishra. On universal approximation and error bounds for fourier neural operators, 2021.
[60] Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. Neural operator: Learning maps between function spaces. arXiv preprint arXiv:2108.08481, 2021.
[61] Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Alexander Pritzel, Suman Ravuri, Timo Ewalds, Ferran Alet, Zach Eaton-Rosen, Weihua Hu, Alexander Merose, Stephan Hoyer, George Holland, Jacklynn Stott, Oriol Vinyals, Shakir Mohamed, and Peter Battaglia. Graphcast: Learning skillful medium-range global weather forecasting, 2022.
[62] A. G. Lamorgese, D. A. Caughey, and S. B. Pope. Direct numerical simulation of homogeneous turbulence with hyperviscosity. Physics of Fluids, 17(1), 12 2004. ISSN 1070-6631. doi: 10.1063/1.1833415. URL https://doi.org/10.1063/1.1833415. 015106.
[63] Vadim Lebedev, Yaroslav Ganin, Maksim Rakhuba, Ivan Oseledets, and Victor Lempitsky. Speeding-up convolutional neural networks using fine-tuned cp-decomposition, 2015.
[64] Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B. Tenenbaum, and Antonio Torralba. Learning particle dynamics for manipulating rigid bodies, deformable objects, and fluids, 2019.
[65] Zijie Li and Amir Barati Farimani. Graph neural network-accelerated lagrangian fluid simulation. Computers & Graphics, 103:201-211, 2022. ISSN 0097-8493. doi: https://doi.org/10.1016/j.cag.2022.02.004. URL https://www.sciencedirect.com/science/article/pii/S0097849322000206.
[66] Zijie Li, Tianqin Li, and Amir Barati Farimani. TPU-GAN: Learning temporal coherence from dynamic point cloud sequences. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=FEBFJ98FKx.
[67] Zijie Li, Kazem Meidani, and Amir Barati Farimani. Transformer for partial differential equations' operator learning. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. URL https://openreview.net/forum?id=EPPqt3uERT.