{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:48:32Z","timestamp":1777873712915,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":55,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22B2018"],"award-info":[{"award-number":["U22B2018"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3736837","type":"proceedings-article","created":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T20:54:17Z","timestamp":1754254457000},"page":"2021-2030","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AGODE: Adaptive Graph ODE for Grid-free Fluid Modeling and Domain Adaptation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2942-5039","authenticated-orcid":false,"given":"Jie","family":"Lv","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi'an, Shaanxi, China and Research Center, Zhejiang Dahua Technology Co.,Ltd., Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4796-5737","authenticated-orcid":false,"given":"Shuyuan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7372-9180","authenticated-orcid":false,"given":"Zhixi","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"U-NO: U-shaped Neural Operators. arXiv e-prints","author":"Rahman Md Ashiqur","year":"2022","unstructured":"Md Ashiqur Rahman, Zachary E Ross, and Kamyar Azizzadenesheli. 2022. U-NO: U-shaped Neural Operators. arXiv e-prints (2022), arXiv-2204."},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Machine Learning. PMLR, 1549-1563","author":"Bakshi Ainesh","year":"2023","unstructured":"Ainesh Bakshi, Allen Liu, Ankur Moitra, and Morris Yau. 2023. Tensor decompositions meet control theory: learning general mixtures of linear dynamical systems. In International Conference on Machine Learning. PMLR, 1549-1563."},{"key":"e_1_3_2_1_3_1","volume-title":"Accurate medium-range global weather forecasting with 3D neural networks. Nature 619, 7970","author":"Bi Kaifeng","year":"2023","unstructured":"Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian. 2023. Accurate medium-range global weather forecasting with 3D neural networks. Nature 619, 7970 (2023), 533-538."},{"key":"e_1_3_2_1_4_1","volume-title":"Accurate medium-range global weather forecasting with 3D neural networks. Nature 619, 7970","author":"Bi Kaifeng","year":"2023","unstructured":"Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian. 2023. Accurate medium-range global weather forecasting with 3D neural networks. Nature 619, 7970 (2023), 533-538."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28670"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28670"},{"key":"e_1_3_2_1_7_1","unstructured":"Peng Chen Yingying Zhang Yunyao Cheng Yang Shu Yihang Wang Qingsong Wen Bin Yang and Chenjuan Guo. 2024. Multi-scale Transformers with Adaptive Pathways for Time Series Forecasting. In ICLR."},{"key":"e_1_3_2_1_8_1","unstructured":"Ricky TQ Chen Yulia Rubanova Jesse Bettencourt and David K Duvenaud. 2018. Neural ordinary differential equations."},{"key":"e_1_3_2_1_9_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=YicbFdNTTy","author":"Dosovitskiy Alexey","year":"2021","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. 2021. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.2.012080"},{"key":"e_1_3_2_1_11_1","volume-title":"The Thirteenth International Conference on Learning Representations.","author":"Fei Zaige","unstructured":"Zaige Fei, Fan Xu, Junyuan Mao, Yuxuan Liang, Qingsong Wen, Kun Wang, Hao Wu, and Yang Wang. [n. d.]. Open-CK: A Large Multi-Physics Fields Coupling benchmarks in Combustion Kinetics. In The Thirteenth International Conference on Learning Representations."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.3402\/tellusa.v56i5.14436"},{"key":"e_1_3_2_1_13_1","volume-title":"Out-of-Domain Generalization in Dynamical Systems Reconstruction. arXiv preprint arXiv:2402.18377","author":"G\u00f6ring Niclas","year":"2024","unstructured":"Niclas G\u00f6ring, Florian Hess, Manuel Brenner, Zahra Monfared, and Daniel Durstewitz. 2024. Out-of-Domain Generalization in Dynamical Systems Reconstruction. arXiv preprint arXiv:2402.18377 (2024)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/qj.3803"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467385"},{"key":"e_1_3_2_1_17_1","volume-title":"andWeiWang","author":"Huang Zijie","year":"2021","unstructured":"Zijie Huang, Yizhou Sun, andWeiWang. 2021. Coupled Graph ODE for Learning Interacting System Dynamics."},{"key":"e_1_3_2_1_18_1","volume-title":"How to find your friendly neighborhood: Graph attention design with self-supervision. arXiv preprint arXiv:2204.04879","author":"Kim Dongkwan","year":"2022","unstructured":"Dongkwan Kim and Alice Oh. 2022. How to find your friendly neighborhood: Graph attention design with self-supervision. arXiv preprint arXiv:2204.04879 (2022)."},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Machine Learning. PMLR, 11283-11301","author":"Kirchmeyer Matthieu","year":"2022","unstructured":"Matthieu Kirchmeyer, Yuan Yin, J\u00e9r\u00e9mie Don\u00e0, Nicolas Baskiotis, Alain Rakotomamonjy, and Patrick Gallinari. 2022. Generalizing to new physical systems via context-informed dynamics model. In International Conference on Machine Learning. PMLR, 11283-11301."},{"key":"e_1_3_2_1_20_1","unstructured":"Vladimir R Kostic Pietro Novelli Riccardo Grazzi Karim Lounici and Massimiliano Pontil. 2024. Learning invariant representations of time-homogeneous stochastic dynamical systems. In ICLR."},{"key":"e_1_3_2_1_21_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 34 (2021), 26548-26560.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 12376-12385","author":"Kundu Jogendra Nath","year":"2020","unstructured":"Jogendra Nath Kundu, Naveen Venkat, Ambareesh Revanur, R Venkatesh Babu, et al. 2020. Towards inheritable models for open-set domain adaptation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 12376-12385."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2023.108354"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482237"},{"key":"e_1_3_2_1_25_1","unstructured":"Jinxi Li Ziyang Song and Bo Yang. 2023. NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos. In NeurIPS."},{"key":"e_1_3_2_1_26_1","volume-title":"Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895","author":"Li Zongyi","year":"2020","unstructured":"Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. 2020. Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895 (2020)."},{"key":"e_1_3_2_1_27_1","unstructured":"Marten Lienen David L\u00fcdke Jan Hansen-Palmus and Stephan G\u00fcnnemann. 2024. From Zero to Turbulence: Generative Modeling for 3D Flow Simulation. In ICLR."},{"key":"e_1_3_2_1_28_1","unstructured":"Phillip Lippe Bastiaan S Veeling Paris Perdikaris Richard E Turner and Johannes Brandstetter. 2023. PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. In NeurIPS."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.powtec.2022.117249"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_2_1_31_1","volume-title":"Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell","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. Nat. Mach. Intell (2021)."},{"key":"e_1_3_2_1_32_1","volume-title":"The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=kuTZMZdCPZ","author":"Luo Xihaier","year":"2024","unstructured":"Xihaier Luo, Wei Xu, Balu Nadiga, Yihui Ren, and Shinjae Yoo. 2024. Continuous Field Reconstruction from Sparse Observations with Implicit Neural Networks. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=kuTZMZdCPZ"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2891257"},{"key":"e_1_3_2_1_34_1","volume-title":"Sai Rajeswar, Kaleem Siddiqi, and Siamak Ravanbakhsh.","author":"Mondal Arnab Kumar","year":"2024","unstructured":"Arnab Kumar Mondal, Siba Smarak Panigrahi, Sai Rajeswar, Kaleem Siddiqi, and Siamak Ravanbakhsh. 2024. Efficient Dynamics Modeling in Interactive Environments with Koopman Theory. In ICLR."},{"key":"e_1_3_2_1_35_1","volume-title":"Learning mesh-based simulation with graph networks. arXiv preprint arXiv:2010.03409","author":"Pfaff Tobias","year":"2020","unstructured":"Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter W Battaglia. 2020. Learning mesh-based simulation with graph networks. arXiv preprint arXiv:2010.03409 (2020)."},{"key":"e_1_3_2_1_36_1","volume-title":"Multistep neural networks for data-driven discovery of nonlinear dynamical systems. arXiv preprint arXiv:1801.01236","author":"Raissi Maziar","year":"2018","unstructured":"Maziar Raissi, Paris Perdikaris, and George Em Karniadakis. 2018. Multistep neural networks for data-driven discovery of nonlinear dynamical systems. arXiv preprint arXiv:1801.01236 (2018)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00685-7"},{"key":"e_1_3_2_1_39_1","volume-title":"Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, and Emmanuel de B\u00e9zenac.","author":"Raonic Bogdan","year":"2024","unstructured":"Bogdan Raonic, Roberto Molinaro, Tim De Ryck, Tobias Rohner, Francesca Bartolucci, Rima Alaifari, Siddhartha Mishra, and Emmanuel de B\u00e9zenac. 2024. Convolutional neural operators for robust and accurate learning of PDEs. Advances in Neural Information Processing Systems 36 (2024)."},{"key":"e_1_3_2_1_40_1","first-page":"234","volume-title":"Munich","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer, 234-241."},{"key":"e_1_3_2_1_41_1","volume-title":"Graph theory based large-scale machine learning with multi-dimensional constrained optimization approaches for exact epidemiological modelling of pandemic diseases","author":"Tutsoy Onder","year":"2023","unstructured":"Onder Tutsoy. 2023. Graph theory based large-scale machine learning with multi-dimensional constrained optimization approaches for exact epidemiological modelling of pandemic diseases. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)."},{"key":"e_1_3_2_1_42_1","unstructured":"Rong Wang Wei Mao and Hongdong Li. 2023. DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation. In NeurIPS."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Yuqing Wang Xiangxian Li Zhuang Qi Jingyu Li Xuelong Li Xiangxu Meng and Lei Meng. 2022. Meta-causal feature learning for out-of-distribution generalization. 530-545.","DOI":"10.1007\/978-3-031-25075-0_36"},{"key":"e_1_3_2_1_44_1","volume-title":"Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias. arXiv preprint arXiv:2505.19038","author":"Wu Hao","year":"2025","unstructured":"Hao Wu, Yuan Gao, Ruiqi Shu, Zean Han, Fan Xu, Zhihong Zhu, Qingsong Wen, XianWu, KunWang, and Xiaomeng Huang. 2025. Turb-L1: Achieving Long-term Turbulence Tracing By Tackling Spectral Bias. arXiv preprint arXiv:2505.19038 (2025)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"HaoWu ChanghuWang Fan Xu Jinbao Xue Chong Chen Xian-Sheng Hua and Xiao Luo. 2024. PURE: Prompt Evolution with Graph ODE for Out-of-distribution Fluid Dynamics Modeling. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=z86knmjoUq","DOI":"10.52202\/079017-3333"},{"key":"e_1_3_2_1_46_1","volume-title":"Proceedings of the 41st International Conference on Machine Learning. PMLR","author":"Wu Hao","year":"2024","unstructured":"Hao Wu, Huiyuan Wang, Kun Wang, Weiyan Wang, Changan Ye, Yangyu Tao, Chong Chen, Xian-Sheng Hua, and Xiao Luo. 2024. Prometheus: Out-of-distribution Fluid Dynamics Modeling with Disentangled Graph ODE. In Proceedings of the 41st International Conference on Machine Learning. PMLR, Vienna, Austria, PMLR 235."},{"key":"e_1_3_2_1_47_1","volume-title":"Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model. AAAI2024","author":"Liang Yuxuan","year":"2023","unstructured":"HaoWu, ShilongWang, Yuxuan Liang, Zhengyang Zhou,Wei Huang,Wei Xiong, and KunWang. 2023. Earthfarseer: Versatile Spatio-Temporal Dynamical Systems Modeling in One Model. AAAI2024 (2023)."},{"key":"e_1_3_2_1_48_1","volume-title":"Spatio-temporal fluid dynamics modeling via physical-awareness and parameter diffusion guidance. arXiv preprint arXiv:2403.13850","author":"Wu Hao","year":"2024","unstructured":"Hao Wu, Fan Xu, Yifan Duan, Ziwei Niu, Weiyan Wang, Gaofeng Lu, Kun Wang, Yuxuan Liang, and Yang Wang. 2024. Spatio-temporal fluid dynamics modeling via physical-awareness and parameter diffusion guidance. arXiv preprint arXiv:2403.13850 (2024)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Hao Wu Shuyi Zhou Xiaomeng Huang and Wei Xiong. 2024. Neural Manifold Operators for Learning the Evolution of Physical Dynamics. https:\/\/openreview.net\/forum?id=SQnOmOzqAM","DOI":"10.1145\/3637528.3671779"},{"key":"e_1_3_2_1_50_1","volume-title":"A survey of efficient fine-tuning methods for Vision-Language Models-Prompt and Adapter. Computers & Graphics","author":"Xing JiaLu","year":"2024","unstructured":"JiaLu Xing, JianPing Liu, JianWang, LuLu Sun, Xi Chen, XunXun Gu, and YingFei Wang. 2024. A survey of efficient fine-tuning methods for Vision-Language Models-Prompt and Adapter. Computers & Graphics (2024)."},{"key":"e_1_3_2_1_51_1","first-page":"7561","article-title":"LEADS: Learning dynamical systems that generalize across environments","volume":"34","author":"Yin Yuan","year":"2021","unstructured":"Yuan Yin, Ibrahim Ayed, Emmanuel de B\u00e9zenac, Nicolas Baskiotis, and Patrick Gallinari. 2021. LEADS: Learning dynamical systems that generalize across environments. Advances in Neural Information Processing Systems 34 (2021), 7561-7573.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_52_1","volume-title":"Joon Young Yang, et al","author":"Yu Youn-Yeol","year":"2024","unstructured":"Youn-Yeol Yu, Jeongwhan Choi, Woojin Cho, Kookjin Lee, Nayong Kim, Kiseok Chang, ChangSeung Woo, Ilho Kim, SeokWoo Lee, Joon Young Yang, et al. 2024. Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer. In ICLR."},{"key":"e_1_3_2_1_53_1","volume-title":"Dynamic graph neural networks under spatio-temporal distribution shift. Advances in neural information processing systems 35","author":"Zhang Zeyang","year":"2022","unstructured":"Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Zhou Qin, and Wenwu Zhu. 2022. Dynamic graph neural networks under spatio-temporal distribution shift. Advances in neural information processing systems 35 (2022), 6074-6089."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107338"},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1515\/ijnsns-2021-0050"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3736837","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:14:03Z","timestamp":1777572843000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3736837"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":55,"alternative-id":["10.1145\/3711896.3736837","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3736837","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}