{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T13:00:51Z","timestamp":1785502851544,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":65,"publisher":"ACM","funder":[{"name":"National Natural Science Foundation of China","award":["62276269"],"award-info":[{"award-number":["62276269"]}]},{"name":"National Natural Science Foundation of China","award":["92270118"],"award-info":[{"award-number":["92270118"]}]},{"name":"Beijing Natural Science Foundation","award":["1232009"],"award-info":[{"award-number":["1232009"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDB0620103"],"award-info":[{"award-number":["XDB0620103"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["202230265"],"award-info":[{"award-number":["202230265"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["E2EG2202X2"],"award-info":[{"award-number":["E2EG2202X2"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770854.3780155","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:07:40Z","timestamp":1785499660000},"page":"1066-1077","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Spatiotemporal Graph Learning with Direct Volumetric Information Passing and Feature Enhancement"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5430-1998","authenticated-orcid":false,"given":"Yuan","family":"Mi","sequence":"first","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4712-3474","authenticated-orcid":false,"given":"Qi","family":"Wang","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2686-2000","authenticated-orcid":false,"given":"Xueqin","family":"Hu","sequence":"additional","affiliation":[{"name":"Wuhan University of Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8401-282X","authenticated-orcid":false,"given":"Yike","family":"Guo","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9777-9676","authenticated-orcid":false,"given":"Ji-Rong","family":"Wen","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0127-4030","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5145-3259","authenticated-orcid":false,"given":"Hao","family":"Sun","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"e_1_3_2_2_2_1","volume-title":"ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations.","author":"Anandkumar Anima","year":"2020","unstructured":"Anima Anandkumar, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Nikola Kovachki, Zongyi Li, Burigede Liu, and Andrew Stuart. 2020. Neural operator: Graph kernel network for partial differential equations. In ICLR 2020 Workshop on Integration of Deep Neural Models and Differential Equations."},{"key":"e_1_3_2_2_3_1","volume-title":"Jean Panaioti Jordanou, Eduardo Rehbein de Souza, and Jomi Fred H\u00fcbner.","author":"Antonelo Eric Aislan","year":"2024","unstructured":"Eric Aislan Antonelo, Eduardo Camponogara, Laio Oriel Seman, Jean Panaioti Jordanou, Eduardo Rehbein de Souza, and Jomi Fred H\u00fcbner. 2024. Physics-informed neural nets for control of dynamical systems. Neurocomputing (2024), 127419."},{"key":"e_1_3_2_2_4_1","volume-title":"U-NO: U-shaped Neural Operators","author":"Rahman Md Ashiqur","year":"2023","unstructured":"Md Ashiqur Rahman, Zachary E Ross, and Kamyar Azizzadenesheli. 2023. U-NO: U-shaped Neural Operators. IEEE Transactions on Machine Learning Research (2023)."},{"key":"e_1_3_2_2_5_1","volume-title":"international conference on machine learning. PMLR, 2402-2411","author":"Avila Belbute-Peres Filipe De","year":"2020","unstructured":"Filipe De Avila Belbute-Peres, Thomas Economon, and Zico Kolter. 2020. Combining differentiable PDE solvers and graph neural networks for fluid flow prediction. In international conference on machine learning. PMLR, 2402-2411."},{"key":"e_1_3_2_2_6_1","volume-title":"International Conference on Machine Learning. PMLR, 1026-1037","author":"Bodnar Cristian","year":"2021","unstructured":"Cristian Bodnar, Fabrizio Frasca, Yuguang Wang, Nina Otter, Guido F Montufar, Pietro Lio, and Michael Bronstein. 2021. Weisfeiler and lehman go topological: Message passing simplicial networks. In International Conference on Machine Learning. PMLR, 1026-1037."},{"key":"e_1_3_2_2_7_1","unstructured":"Johannes Brandstetter Daniel Worrall and Max Welling. 2022. Message passing neural PDE solvers. (2022)."},{"key":"e_1_3_2_2_8_1","volume-title":"How attentive are graph attention networks?","author":"Brody Shaked","year":"2022","unstructured":"Shaked Brody, Uri Alon, and Eran Yahav. 2022. How attentive are graph attention networks? (2022)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4050542"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1006\/jcph.1998.6151"},{"key":"e_1_3_2_2_12_1","volume-title":"Finite","author":"Eymard Robert","year":"2000","unstructured":"Robert Eymard, Thierry Gallou\u00ebt, and Rapha\u00e8le Herbin. 2000. Finite volume methods. Handbook of numerical analysis, Vol. 7 (2000), 713-1018."},{"key":"e_1_3_2_2_13_1","volume-title":"Multiscale meshgraphnets. arXiv preprint arXiv:2210.00612","author":"Fortunato Meire","year":"2022","unstructured":"Meire Fortunato, Tobias Pfaff, Peter Wirnsberger, Alexander Pritzel, and Peter Battaglia. 2022. Multiscale meshgraphnets. arXiv preprint arXiv:2210.00612 (2022)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.110079"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.114502"},{"key":"e_1_3_2_2_16_1","volume-title":"International conference on machine learning. PMLR, 1263-1272","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International conference on machine learning. PMLR, 1263-1272."},{"key":"e_1_3_2_2_17_1","volume-title":"Finite difference method for numerical computation of discontinuous solutions of the equations of fluid dynamics. Matemati\u010deskij sbornik","author":"Godunov Sergei K","year":"1959","unstructured":"Sergei K Godunov and I Bohachevsky. 1959. Finite difference method for numerical computation of discontinuous solutions of the equations of fluid dynamics. Matemati\u010deskij sbornik, Vol. 47, 3 (1959), 271-306."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783275"},{"key":"e_1_3_2_2_19_1","volume-title":"Multiwavelet-based operator learning for differential equations. Advances in neural information processing systems","author":"Gupta Gaurav","year":"2021","unstructured":"Gaurav Gupta, Xiongye Xiao, and Paul Bogdan. 2021. Multiwavelet-based operator learning for differential equations. Advances in neural information processing systems, Vol. 34 (2021), 24048-24062."},{"key":"e_1_3_2_2_20_1","volume-title":"Tolga Birdal, Tamal K Dey, Soham Mukherjee, Shreyas N Samaga, et al.","author":"Hajij Mustafa","year":"2022","unstructured":"Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzm\u00e1n-S\u00e1enz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal K Dey, Soham Mukherjee, Shreyas N Samaga, et al., 2022. Topological deep learning: Going beyond graph data. arXiv preprint arXiv:2206.00606 (2022)."},{"key":"e_1_3_2_2_21_1","volume-title":"International Conference on Machine Learning. PMLR, 12556-12569","author":"Hao Zhongkai","year":"2023","unstructured":"Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jian Song, and Jun Zhu. 2023. Gnot: A general neural operator transformer for operator learning. In International Conference on Machine Learning. PMLR, 12556-12569."},{"key":"e_1_3_2_2_22_1","volume-title":"International Conference on Artificial Intelligence and Statistics. PMLR, 3034-3047","author":"He Di","year":"2023","unstructured":"Di He, Shanda Li, Wenlei Shi, Xiaotian Gao, Jia Zhang, Jiang Bian, Liwei Wang, and Tie-Yan Liu. 2023. Learning physics-informed neural networks without stacked back-propagation. In International Conference on Artificial Intelligence and Statistics. PMLR, 3034-3047."},{"key":"e_1_3_2_2_23_1","volume-title":"Graph Neural PDE Solvers with Conservation and Similarity-Equivariance. arXiv preprint arXiv:2405.16183","author":"Horie Masanobu","year":"2024","unstructured":"Masanobu Horie and Naoto Mitsume. 2024. Graph Neural PDE Solvers with Conservation and Similarity-Equivariance. arXiv preprint arXiv:2405.16183 (2024)."},{"key":"e_1_3_2_2_24_1","volume-title":"The finite element method: linear static and dynamic finite element analysis","author":"Hughes Thomas JR","unstructured":"Thomas JR Hughes. 2012. The finite element method: linear static and dynamic finite element analysis. Courier Corporation."},{"key":"e_1_3_2_2_25_1","volume-title":"Eagle: Large-scale learning of turbulent fluid dynamics with mesh transformers. arXiv preprint arXiv:2302.10803","author":"Janny Steeven","year":"2023","unstructured":"Steeven Janny, Aur\u00e9lien Beneteau, Madiha Nadri, Julie Digne, Nicolas Thome, and Christian Wolf. 2023. Eagle: Large-scale learning of turbulent fluid dynamics with mesh transformers. arXiv preprint arXiv:2302.10803 (2023)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25556"},{"key":"e_1_3_2_2_27_1","volume-title":"Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations.","author":"Diederik","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-49411-w"},{"key":"e_1_3_2_2_29_1","first-page":"1","article-title":"Neural operator: Learning maps between function spaces with applications to pdes","volume":"24","author":"Kovachki Nikola","year":"2023","unstructured":"Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. 2023. Neural operator: Learning maps between function spaces with applications to pdes. Journal of Machine Learning Research, Vol. 24, 89 (2023), 1-97.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_30_1","first-page":"26548","article-title":"Characterizing possible failure modes in physics-informed neural networks","volume":"34","author":"Krishnapriyan Aditi","year":"2021","unstructured":"Aditi Krishnapriyan, Amir Gholami, Shandian Zhe, Robert Kirby, and Michael W Mahoney. 2021. Characterizing possible failure modes in physics-informed neural networks. Advances in Neural Information Processing Systems, Vol. 34 (2021), 26548-26560.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_31_1","first-page":"1","article-title":"Fourier neural operator with learned deformations for pdes on general geometries","volume":"24","author":"Li Zongyi","year":"2023","unstructured":"Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, and Anima Anandkumar. 2023a. Fourier neural operator with learned deformations for pdes on general geometries. Journal of Machine Learning Research, Vol. 24, 388 (2023), 1-26.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_32_1","first-page":"6755","article-title":"Multipole graph neural operator for parametric partial differential equations","volume":"33","author":"Li Zongyi","year":"2020","unstructured":"Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew Stuart, Kaushik Bhattacharya, and Anima Anandkumar. 2020. Multipole graph neural operator for parametric partial differential equations. Advances in Neural Information Processing Systems, Vol. 33 (2020), 6755-6766.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_33_1","volume-title":"Maximilian Stadler, Christian Hundt, Kamyar Azizzadenesheli, et al.","author":"Li Zongyi","year":"2024","unstructured":"Zongyi Li, Nikola Kovachki, Chris Choy, Boyi Li, Jean Kossaifi, Shourya Otta, Mohammad Amin Nabian, Maximilian Stadler, Christian Hundt, Kamyar Azizzadenesheli, et al., 2024a. Geometry-informed neural operator for large-scale 3d pdes. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_34_1","volume-title":"Fourier Neural Operator for Parametric Partial Differential Equations. In International Conference on Learning Representations.","author":"Li Z.","unstructured":"Z. Li, N. B. Kovachki, K. Azizzadenesheli, B. Liu, K. Bhattacharya, A. Stuart, and A. Anandkumar. 2021a. Fourier Neural Operator for Parametric Partial Differential Equations. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_35_1","volume-title":"Transformer for partial differential equations' operator learning","author":"Li Zijie","year":"2023","unstructured":"Zijie Li, Kazem Meidani, and Amir Barati Farimani. 2023b. Transformer for partial differential equations' operator learning. IEEE Transactions on Machine Learning Research (2023)."},{"key":"e_1_3_2_2_36_1","volume-title":"Physics-informed neural operator for learning partial differential equations. ACM\/JMS Journal of Data Science","author":"Li Zongyi","year":"2021","unstructured":"Zongyi Li, Hongkai Zheng, Nikola Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, and Anima Anandkumar. 2021b. Physics-informed neural operator for learning partial differential equations. ACM\/JMS Journal of Data Science (2021)."},{"key":"e_1_3_2_2_37_1","first-page":"1","article-title":"Physics-informed neural operator for learning partial differential equations","volume":"1","author":"Li Zongyi","year":"2024","unstructured":"Zongyi Li, Hongkai Zheng, Nikola Kovachki, David Jin, Haoxuan Chen, Burigede Liu, Kamyar Azizzadenesheli, and Anima Anandkumar. 2024b. Physics-informed neural operator for learning partial differential equations. ACM\/JMS Journal of Data Science, Vol. 1, 3 (2024), 1-27.","journal-title":"ACM\/JMS Journal of Data Science"},{"key":"e_1_3_2_2_38_1","volume-title":"International Conference on Learning Representations","author":"Lienen Marten","year":"2022","unstructured":"Marten Lienen and Stephan G\u00fcnnemann. 2022. Learning the dynamics of physical systems from sparse observations with finite element networks. International Conference on Learning Representations (2022)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_3_2_2_40_1","volume-title":"Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nature machine intelligence","author":"Lu Lu","year":"2021","unstructured":"Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, and George Em Karniadakis. 2021. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nature machine intelligence, Vol. 3, 3 (2021), 218-229."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709244"},{"key":"e_1_3_2_2_42_1","first-page":"1","article-title":"Physics-informed neural networks for power systems. In 2020 IEEE power & energy society general meeting (PESGM)","author":"Misyris George S","year":"2020","unstructured":"George S Misyris, Andreas Venzke, and Spyros Chatzivasileiadis. 2020. Physics-informed neural networks for power systems. In 2020 IEEE power & energy society general meeting (PESGM). IEEE, 1-5.","journal-title":"IEEE"},{"key":"e_1_3_2_2_43_1","volume-title":"Marcelo J Cola\u00e7o, and Renato M Cotta.","author":"\u00d6zi\u015fik M Necati","year":"2017","unstructured":"M Necati \u00d6zi\u015fik, Helcio RB Orlande, Marcelo J Cola\u00e7o, and Renato M Cotta. 2017. Finite difference methods in heat transfer. CRC press."},{"key":"e_1_3_2_2_44_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al., 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2024.117152"},{"key":"e_1_3_2_2_46_1","volume-title":"Learning Mesh-Based Simulation with Graph Networks. In International Conference on Learning Representations.","author":"Pfaff T.","unstructured":"T. Pfaff, M. Fortunato, A. Sanchez-Gonzalez, and P. Battaglia. 2021. Learning Mesh-Based Simulation with Graph Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_47_1","volume-title":"Approximation theory of the MLP model in neural networks. Acta numerica","author":"Pinkus Allan","year":"1999","unstructured":"Allan Pinkus. 1999. Approximation theory of the MLP model in neural networks. Acta numerica, Vol. 8 (1999), 143-195."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00685-7"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1022375915113"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.114399"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphy.2020.00042"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1029\/2018GL080704"},{"key":"e_1_3_2_2_54_1","article-title":"Can deep learning beat numerical weather prediction","volume":"379","author":"Schultz Martin G","year":"2021","unstructured":"Martin G Schultz, Clara Betancourt, Bing Gong, Felix Kleinert, Michael Langguth, Lukas Hubert Leufen, Amirpasha Mozaffari, and Scarlet Stadtler. 2021. Can deep learning beat numerical weather prediction? Philosophical Transactions of the Royal Society A, Vol. 379, 2194 (2021), 20200097.","journal-title":"Philosophical Transactions of the Royal Society A"},{"key":"e_1_3_2_2_55_1","first-page":"194","article-title":"Lstm neural networks for language modeling","volume":"2012","author":"Sundermeyer Martin","year":"2012","unstructured":"Martin Sundermeyer, Ralf Schl\u00fcter, and Hermann Ney. 2012. Lstm neural networks for language modeling.. In Interspeech, Vol. 2012. 194-197.","journal-title":"Interspeech"},{"key":"e_1_3_2_2_56_1","volume-title":"Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems","author":"Tolstikhin Ilya O","year":"2021","unstructured":"Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al., 2021. Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems, Vol. 34 (2021), 24261-24272."},{"key":"e_1_3_2_2_57_1","volume-title":"International Conference on Learning Representations","author":"Tran Alasdair","year":"2021","unstructured":"Alasdair Tran, Alexander Mathews, Lexing Xie, and Cheng Soon Ong. 2021. Factorized fourier neural operators. International Conference on Learning Representations (2021)."},{"key":"e_1_3_2_2_58_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_59_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio et al. 2017. Graph attention networks. Vol. 1050 20 (2017) 10-48550."},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2018.01.036"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.advwatres.2022.104180"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00023"},{"key":"e_1_3_2_2_63_1","volume-title":"Transolver: A fast transformer solver for pdes on general geometries. arXiv preprint arXiv:2402.02366","author":"Wu Haixu","year":"2024","unstructured":"Haixu Wu, Huakun Luo, Haowen Wang, Jianmin Wang, and Mingsheng Long. 2024. Transolver: A fast transformer solver for pdes on general geometries. arXiv preprint arXiv:2402.02366 (2024)."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2019.05.006"},{"key":"e_1_3_2_2_65_1","volume-title":"Purely satellite data-driven deep learning forecast of complicated tropical instability waves. Science advances","author":"Zheng Gang","year":"2020","unstructured":"Gang Zheng, Xiaofeng Li, Rong-Hua Zhang, and Bin Liu. 2020. Purely satellite data-driven deep learning forecast of complicated tropical instability waves. Science advances, Vol. 6, 29 (2020), eaba1482."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3770854.3780155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:07:51Z","timestamp":1785499671000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770854.3780155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":65,"alternative-id":["10.1145\/3770854.3780155","10.1145\/3770854"],"URL":"https:\/\/doi.org\/10.1145\/3770854.3780155","relation":{},"subject":[],"published":{"date-parts":[[2026,4,20]]},"assertion":[{"value":"2026-04-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}