{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T21:17:58Z","timestamp":1787692678652,"version":"build-2784847793"},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","funder":[{"name":"National Natural Science Foundation of China","award":["62576013"],"award-info":[{"award-number":["62576013"]}]},{"name":"National Key Research and Development of China","award":["2024YFC2607404"],"award-info":[{"award-number":["2024YFC2607404"]}]},{"name":"Jiangsu Provincial Key Research and Development Program","award":["BE2022065-1, BE2022065-3"],"award-info":[{"award-number":["BE2022065-1, BE2022065-3"]}]},{"name":"Ningxia Domain-Specific Large Model Health Industry R&D Program","award":["2024JBGS001"],"award-info":[{"award-number":["2024JBGS001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770854.3783959","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:07:40Z","timestamp":1785499660000},"page":"2458-2469","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["FilDeep: Learning Large Deformations of Elastic-Plastic Solids with Multi-Fidelity Data"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4762-5943","authenticated-orcid":false,"given":"Jianheng","family":"Tang","sequence":"first","affiliation":[{"name":"School of Computer Science, Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2497-3413","authenticated-orcid":false,"given":"Shilong","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8682-3769","authenticated-orcid":false,"given":"Zhe","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8784-6963","authenticated-orcid":false,"given":"Haonan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1306-3169","authenticated-orcid":false,"given":"Menglu","family":"Wang","sequence":"additional","affiliation":[{"name":"Fuyao Group, Fuqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2141-6553","authenticated-orcid":false,"given":"Zhanxing","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Science, University of Southampton, Southampton, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1180-8078","authenticated-orcid":false,"given":"Yunhuai","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Peking University, Beijing, China and Key Lab of High Confidence Software Technologies (Peking University), Ministry of Education, 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","volume-title":"Neural operators for accelerating scientific simulations and design. Nature Reviews Physics","author":"Azizzadenesheli Kamyar","year":"2024","unstructured":"Kamyar Azizzadenesheli, Nikola Kovachki, Zongyi Li, Miguel Liu-Schiaffini, Jean Kossaifi, and Anima Anandkumar. 2024. Neural operators for accelerating scientific simulations and design. Nature Reviews Physics (2024), 1-9."},{"key":"e_1_3_2_2_2_1","volume-title":"Layer normalization. arXiv preprint arXiv:1607.06450","author":"Jimmy Lei Ba.","year":"2016","unstructured":"Jimmy Lei Ba. 2016. Layer normalization. arXiv preprint arXiv:1607.06450 (2016)."},{"key":"e_1_3_2_2_3_1","volume-title":"Finite element analysis of composite materials using Abaqus\u00ae","author":"Barbero Ever J","unstructured":"Ever J Barbero. 2023. Finite element analysis of composite materials using Abaqus\u00ae. CRC press."},{"key":"e_1_3_2_2_4_1","volume-title":"Finite","author":"Barth Timothy","unstructured":"Timothy Barth, Rapha\u00e8le Herbin, and Mario Ohlberger. 2018. Finite volume methods: foundation and analysis. Encyclopedia of computational mechanics second edition (2018), 1-60."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1903070116"},{"key":"e_1_3_2_2_6_1","volume-title":"Elasticity and plasticity of large deformations: including gradient materials","author":"Bertram Albrecht","unstructured":"Albrecht Bertram. 2021. Elasticity and plasticity of large deformations: including gradient materials. Springer Nature."},{"key":"e_1_3_2_2_7_1","volume-title":"International conference on machine learning. PMLR, 2806-2823","author":"Bonev Boris","year":"2023","unstructured":"Boris Bonev, Thorsten Kurth, Christian Hundt, Jaideep Pathak, Maximilian Baust, Karthik Kashinath, and Anima Anandkumar. 2023. Spherical fourier neural operators: Learning stable dynamics on the sphere. In International conference on machine learning. PMLR, 2806-2823."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"AF Bower. 2009. Applied Mechanics of Solids.","DOI":"10.1201\/9781439802489"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0924-0136(99)00487-2"},{"key":"e_1_3_2_2_10_1","unstructured":"Owen Davis Mohammad Motamed and Raul Tempone. 2023. Residual Multi-Fidelity Neural Network Computing. arXiv:2310.03572 [cs.LG]"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2024.3506482"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3347746"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.3934\/acse.2023015"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-99693-6"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.2007.1900"},{"key":"e_1_3_2_2_16_1","volume-title":"Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities. Computer methods in applied mechanics and engineering","author":"Guo Mengwu","year":"2022","unstructured":"Mengwu Guo, Andrea Manzoni, Maurice Amendt, Paolo Conti, and Jan S Hesthaven. 2022. Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities. Computer methods in applied mechanics and engineering, Vol. 389 (2022), 114378."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-1596-2"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2023.112462"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2025.3565374"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4062966"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-021-00314-5"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-021-00314-5"},{"key":"e_1_3_2_2_24_1","volume-title":"Multi-grid tensorized Fourier neural operator for high-resolution PDEs. arXiv preprint arXiv:2310.00120","author":"Kossaifi Jean","year":"2023","unstructured":"Jean Kossaifi, Nikola Kovachki, Kamyar Azizzadenesheli, and Anima Anandkumar. 2023. Multi-grid tensorized Fourier neural operator for high-resolution PDEs. arXiv preprint arXiv:2310.00120 (2023)."},{"key":"e_1_3_2_2_25_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. 2023. 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_26_1","volume-title":"Fourier Neural Operator for Parametric Partial Differential Equations. In International Conference on Learning Representations.","author":"Li Zongyi","year":"2021","unstructured":"Zongyi Li, Nikola Borislavov Kovachki, Kamyar Azizzadenesheli, Burigede liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar. 2021. Fourier Neural Operator for Parametric Partial Differential Equations. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1115\/1.4044400"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511569340"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1922210117"},{"key":"e_1_3_2_2_30_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_31_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.4.023210"},{"key":"e_1_3_2_2_32_1","volume-title":"Physics of Fluids","volume":"35","author":"Lyu Yanfang","year":"2023","unstructured":"Yanfang Lyu, Xiaoyu Zhao, Zhiqiang Gong, Xiao Kang, and Wen Yao. 2023. Multi-fidelity prediction of fluid flow based on transfer learning using Fourier neural operator. Physics of Fluids, Vol. 35, 7 (2023)."},{"key":"e_1_3_2_2_33_1","volume-title":"Deep Learning: A Critical Appraisal. arXiv preprint arXiv:1801.00631","author":"Marcus Gary","year":"2018","unstructured":"Gary Marcus. 2018. Deep Learning: A Critical Appraisal. arXiv preprint arXiv:1801.00631 (2018)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/0020-7683(75)90033-5"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2019.109020"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1005-0302(12)60016-4"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2017.01.047"},{"key":"e_1_3_2_2_38_1","volume-title":"A review on simulation in digital twin for aerospace, manufacturing and robotics. Materials today: proceedings","author":"Phanden Rakesh Kumar","year":"2021","unstructured":"Rakesh Kumar Phanden, Priavrat Sharma, and Anubhav Dubey. 2021. A review on simulation in digital twin for aerospace, manufacturing and robotics. Materials today: proceedings, Vol. 38 (2021), 174-178."},{"key":"e_1_3_2_2_39_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_40_1","volume-title":"U-no: U-shaped neural operators. arXiv preprint arXiv:2204.11127","author":"Rahman Md Ashiqur","year":"2022","unstructured":"Md Ashiqur Rahman, Zachary E Ross, and Kamyar Azizzadenesheli. 2022. U-no: U-shaped neural operators. arXiv preprint arXiv:2204.11127 (2022)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Barna Szab\u00f3 and Ivo Babu\u0161ka. 2021. Finite element analysis: Method verification and validation. (2021).","DOI":"10.1002\/9781119426479"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2025.3560038"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2025.3626742"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3737238"},{"key":"e_1_3_2_2_45_1","volume-title":"Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems. arXiv preprint arXiv:2506.06021","author":"Tao Shilong","year":"2025","unstructured":"Shilong Tao, Zhe Feng, Haonan Sun, Zhanxing Zhu, and Yunhuai Liu. 2025b. Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems. arXiv preprint arXiv:2506.06021 (2025)."},{"key":"e_1_3_2_2_46_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_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10915-022-01881-0"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2020.05.022"},{"key":"e_1_3_2_2_49_1","volume-title":"International Conference on Machine Learning. PMLR, 37624-37634","author":"Wu Dongxia","year":"2023","unstructured":"Dongxia Wu, Ruijia Niu, Matteo Chinazzi, Yian Ma, and Rose Yu. 2023. Disentangled multi-fidelity deep bayesian active learning. In International Conference on Machine Learning. PMLR, 37624-37634."},{"key":"e_1_3_2_2_50_1","volume-title":"Proceedings of the 41st International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"53705","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. In Proceedings of the 41st International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 235). PMLR, 53681-53705."}],"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\/3770854.3783959","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:18:11Z","timestamp":1787689091000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770854.3783959"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":50,"alternative-id":["10.1145\/3770854.3783959","10.1145\/3770854"],"URL":"https:\/\/doi.org\/10.1145\/3770854.3783959","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"}}]}}