[1] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[2] Limin Huang, Yu Jing, Hangyu Chen, Lu Zhang, and Yuliang Liu. A regional wind wave prediction surrogate model based on cnn deep learning network. Applied Ocean Research, 126:103287, 2022.
[3] Anima Anandkumar, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Nikola Kovachki, Zongyi Li, Burigede Liu, and Andrew Stuart. Neural operator: Graph kernel network for partial differential equations. In ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations, 2020.
[4] Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, and Aditya Grover. Climax: A foundation model for weather and climate. arXiv preprint arXiv:2301.10343, 2023.
[5] Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran. Image transformer. In International conference on machine learning, pages 4055-4064. PMLR, 2018.
[6] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3202-3211, 2022.
[7] Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim. Graph transformer networks. Advances in neural information processing systems, 32, 2019.
[8] AG Dekker, Žamurović-Nenad, HJ Hoogenboom, and SWM Peters. Remote sensing, ecological water quality modelling and in situ measurements: a case study in shallow lakes. Hydrological Sciences Journal, 41(4):531-547, 1996.
[9] Jin Li and Andrew D Heap. A review of spatial interpolation methods for environmental scientists. Geoscience Australia, Record. Geoscience Australia, Canberra, 2008.
[10] David W Wong, Lester Yuan, and Susan A Perlin. Comparison of spatial interpolation methods for the estimation of air quality data. Journal of Exposure Science & Environmental Epidemiology, 14(5):404-415, 2004.
[11] B Siddani and S Balachandar. Point-particle drag, lift, and torque closure models using machine learning: Hierarchical approach and interpretability. Physical Review Fluids, 8(1):014303, 2023.
[12] Volker Springel. High performance computing and numerical modelling, 2014.
[13] Eu Gene Chung, Fabián A Bombardelli, and S Geoffrey Schladow. Modeling linkages between sediment resuspension and water quality in a shallow, eutrophic, wind-exposed lake. Ecological Modelling, 220(9-10):1251-1265, 2009.
[14] Zhirui Deng, Qing He, Zeinab Safar, and Claire Chassagne. The role of algae in fine sediment flocculation: In-situ and laboratory measurements. Marine Geology, 413:71-84, 2019.
[15] Song Yang, Jiamou Liu, and Kaiqi Zhao. Getnext: trajectory flow map enhanced transformer for next poi recommendation. In Proceedings of the 45th International ACM SIGIR Conference on research and development in information retrieval, pages 1144-1153, 2022.
[16] Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao, and Chenxing Wang. Next point-of-interest recommendation with auto-correlation enhanced multi-modal transformer network. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2612-2616, 2022.
[17] Iz Beltagy, Matthew E Peters, and Arman Cohan. Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150, 2020.
[18] Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov. Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860, 2019.
[19] Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh. Set transformer: A framework for attention-based permutation-invariant neural networks. In International conference on machine learning, pages 3744-3753. PMLR, 2019.