[20] Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira. Perceiver: General perception with iterative attention. In International conference on machine learning, pages 4651-4664. PMLR, 2021.
[21] Nikita Kitaev, Lukasz Kaiser, and Anselm Levskaya. Reformer: The efficient transformer. arXiv preprint arXiv:2001.04451, 2020.
[22] Shitao Tang, Jiahui Zhang, Siyu Zhu, and Ping Tan. Quadtree attention for vision transformers. arXiv preprint arXiv:2201.02767, 2022.
[23] Zhi Cheng, Xiu Su, Xueyu Wang, Shan You, and Chang Xu. Sufficient vision transformer. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 190–200, 2022.
[24] Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, and Tim Salimans. Axial attention in multidimensional transformers. arXiv preprint arXiv:1912.12180, 2019.
[25] Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh. Dynamicvit: Efficient vision transformers with dynamic token sparsification. Advances in neural information processing systems, 34:13937-13949, 2021.
[26] Qitian Wu, Wentao Zhao, Zenan Li, David P Wipf, and Junchi Yan. Nodeformer: A scalable graph structure learning transformer for node classification. Advances in Neural Information Processing Systems, 35:27387-27401, 2022.
[27] Jinsong Chen, Kaiyuan Gao, Gaichao Li, and Kun He. Nagphormer: A tokenized graph transformer for node classification in large graphs. In The Eleventh International Conference on Learning Representations, 2022.
[28] Zaixi Zhang, Qi Liu, Qingyong Hu, and Chee-Kong Lee. Hierarchical graph transformer with adaptive node sampling. Advances in Neural Information Processing Systems, 35:21171-21183, 2022.
[29] Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou. Rethinking graph transformers with spectral attention. Advances in Neural Information Processing Systems, 34:21618-21629, 2021.
[30] Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, and Junchi Yan. Differmer: Scalable (graph) transformers induced by energy constrained diffusion. arXiv preprint arXiv:2301.09474, 2023.
[31] Nikola B Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M Stuart, and Anima Anandkumar. Neural operator: Learning maps between function spaces with applications to pdes. J. Mach. Learn. Res., 24(89):1-97, 2023.
[32] Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew Stuart, Kaushik Bhattacharya, and Anima Anandkumar. Multipole graph neural operator for parametric partial differential equations. Advances in Neural Information Processing Systems, 33:6755-6766, 2020.
[33] Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895, 2020.
[34] John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, and Bryan Catanzaro. Adaptive fourier neural operators: Efficient token mixers for transformers. arXiv preprint arXiv:2111.13587, 2021.
[35] Shuhao Cao. Choose a transformer: Fourier or galerkin. Advances in neural information processing systems, 34:24924-24940, 2021.
[36] Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, et al. Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv:2202.11214, 2022.
[37] Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian. Accurate medium-range global weather forecasting with 3d neural networks. Nature, 619(7970):533-538, 2023.
[38] Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, and Aditya Grover. Climax: A foundation model for weather and climate, january 2023. arXiv preprint arXiv:2301.10343, 2023.
[39] Shaowu Pan, Steven L Brunton, and J Nathan Kutz. Neural implicit flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data, 2022.
[40] Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, and Patrick Gallinari. Continuous pde dynamics forecasting with implicit neural representations. arXiv preprint arXiv:2209.14855, 2022.
[41] Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, Yue Chang, GA Pershing, Henrique Teles Maia, Maurizio Chiaramonte, Kevin Carlberg, and Eitan Grinspun. Crom: Continuous reduced-order modeling of pdes using implicit neural representations. arXiv preprint arXiv:2206.02607, 2022.