{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T04:50:29Z","timestamp":1785905429562,"version":"3.56.0"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000083","name":"National Science Foundation Directorate for Computer and Information Science and Engineering","doi-asserted-by":"publisher","award":["2229155"],"award-info":[{"award-number":["2229155"]}],"id":[{"id":"10.13039\/100000083","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.114231","type":"journal-article","created":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:33:42Z","timestamp":1781278422000},"page":"114231","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["Elasticity-Aware Neural Hamiltonian Fields for dynamic 3D vision synthesis"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8232-4911","authenticated-orcid":false,"given":"Wenkai","family":"Tan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0199-1231","authenticated-orcid":false,"given":"Ziyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8119-7911","authenticated-orcid":false,"given":"Safayat Bin","family":"Hakim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6757-105X","authenticated-orcid":false,"given":"Alvaro","family":"Velasquez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4323-2632","authenticated-orcid":false,"given":"Lusi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2631-9223","authenticated-orcid":false,"given":"Houbing","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.114231_b1","series-title":"ACM SIGGRAPH 2012 Courses","article-title":"FEM simulation of 3D deformable solids: a practitioner\u2019s guide to theory, discretization and model reduction","author":"Sifakis","year":"2012"},{"key":"10.1016\/j.patcog.2026.114231_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101569","article-title":"Simulation of hyperelastic materials in real-time using deep learning","volume":"59","author":"Mendizabal","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.patcog.2026.114231_b3","series-title":"Seminal Graphics Papers: Pushing the Boundaries, Volume 2","first-page":"787","article-title":"Projective dynamics: Fusing constraint projections for fast simulation","author":"Bouaziz","year":"2023"},{"issue":"6","key":"10.1016\/j.patcog.2026.114231_b4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3355089.3356486","article-title":"A scalable galerkin multigrid method for real-time simulation of deformable objects","volume":"38","author":"Xian","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.patcog.2026.114231_b5","series-title":"2025 IEEE\/RSJ International Conference on Intelligent Robots and Systems","first-page":"11247","article-title":"Cressim\u2013MPM: A material point method library for surgical soft body simulation with cutting and suturing","author":"Ou","year":"2025"},{"issue":"2","key":"10.1016\/j.patcog.2026.114231_b6","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.jvcir.2007.01.005","article-title":"Position based dynamics","volume":"18","author":"M\u00fcller","year":"2007","journal-title":"J. Vis. Commun. Image Represent."},{"key":"10.1016\/j.patcog.2026.114231_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110758","article-title":"Efficient neural implicit representation for 3D human reconstruction","volume":"156","author":"Huang","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114231_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111271","article-title":"Generalizable 3D Gaussian splatting for novel view synthesis","volume":"161","author":"Zhao","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114231_b9","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111426","article-title":"3D points splatting for real-time dynamic hand reconstruction","volume":"162","author":"Jiang","year":"2025","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114231_b10","series-title":"International Conference on Machine Learning","first-page":"8459","article-title":"Learning to simulate complex physics with graph networks","author":"Sanchez-Gonzalez","year":"2020"},{"key":"10.1016\/j.patcog.2026.114231_b11","unstructured":"T. Pfaff, M. Fortunato, A. Sanchez-Gonzalez, P. Battaglia, Learning mesh-based simulation with graph networks, in: International Conference on Learning Representations, 2020."},{"issue":"388","key":"10.1016\/j.patcog.2026.114231_b12","first-page":"1","article-title":"Fourier neural operator with learned deformations for pdes on general geometries","volume":"24","author":"Li","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.patcog.2026.114231_b13","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","article-title":"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","volume":"378","author":"Raissi","year":"2019","journal-title":"J. Comput. Phys."},{"issue":"6","key":"10.1016\/j.patcog.2026.114231_b14","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1038\/s42254-021-00314-5","article-title":"Physics-informed machine learning","volume":"3","author":"Karniadakis","year":"2021","journal-title":"Nat. Rev. Phys."},{"issue":"1","key":"10.1016\/j.patcog.2026.114231_b15","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1145\/3503250","article-title":"Nerf: Representing scenes as neural radiance fields for view synthesis","volume":"65","author":"Mildenhall","year":"2021","journal-title":"Commun. ACM"},{"key":"10.1016\/j.patcog.2026.114231_b16","doi-asserted-by":"crossref","unstructured":"Y. Feng, Y. Shang, X. Li, T. Shao, C. Jiang, Y. Yang, Pie-nerf: Physics-based interactive elastodynamics with nerf, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 4450\u20134461.","DOI":"10.1109\/CVPR52733.2024.00426"},{"key":"10.1016\/j.patcog.2026.114231_b17","first-page":"379","article-title":"Latent-space dynamics for reduced deformable simulation","volume":"vol. 38","author":"Fulton","year":"2019"},{"key":"10.1016\/j.patcog.2026.114231_b18","doi-asserted-by":"crossref","unstructured":"L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, A. Geiger, Occupancy networks: Learning 3d reconstruction in function space, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 4460\u20134470.","DOI":"10.1109\/CVPR.2019.00459"},{"key":"10.1016\/j.patcog.2026.114231_b19","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/LCSYS.2023.3344286","article-title":"Neural autoencoder-based structure-preserving model order reduction and control design for high-dimensional physical systems","volume":"8","author":"Lepri","year":"2023","journal-title":"IEEE Control. Syst. Lett."},{"key":"10.1016\/j.patcog.2026.114231_b20","series-title":"Nonlinear Continuum Mechanics for Finite Element Analysis","author":"Bonet","year":"1997"},{"issue":"13","key":"10.1016\/j.patcog.2026.114231_b21","doi-asserted-by":"crossref","first-page":"2874","DOI":"10.1002\/nme.6336","article-title":"A matrix-free approach for finite-strain hyperelastic problems using geometric multigrid","volume":"121","author":"Davydov","year":"2020","journal-title":"Internat. J. Numer. Methods Engrg."},{"key":"10.1016\/j.patcog.2026.114231_b22","unstructured":"A. Toshev, J.A. Erbesdobler, N.A. Adams, J. Brandstetter, Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics, in: ICLR 2024 Workshop on AI4DifferentialEquations in Science, 2024."},{"key":"10.1016\/j.patcog.2026.114231_b23","article-title":"Interaction networks for learning about objects, relations and physics","volume":"29","author":"Battaglia","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"5","key":"10.1016\/j.patcog.2026.114231_b24","doi-asserted-by":"crossref","DOI":"10.4208\/cicp.OA-2020-0164","article-title":"Extended physics-informed neural networks (XPINNs): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations","volume":"28","author":"Jagtap","year":"2020","journal-title":"Commun. Comput. Phys."},{"issue":"4","key":"10.1016\/j.patcog.2026.114231_b25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3528223.3530169","article-title":"Physics informed neural fields for smoke reconstruction with sparse data","volume":"41","author":"Chu","year":"2022","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"10.1016\/j.patcog.2026.114231_b26","article-title":"Neural ordinary differential equations","volume":"31","author":"Chen","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114231_b27","unstructured":"M. Lutter, C. Ritter, J. Peters, Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning, in: International Conference on Learning Representations, 2019."},{"key":"10.1016\/j.patcog.2026.114231_b28","article-title":"Hamiltonian neural networks","volume":"32","author":"Greydanus","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114231_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcp.2023.112495","article-title":"Symplectic learning for Hamiltonian neural networks","volume":"494","author":"David","year":"2023","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.patcog.2026.114231_b30","unstructured":"S. Xiong, Y. Tong, X. He, S. Yang, C. Yang, B. Zhu, Nonseparable Symplectic Neural Networks, in: International Conference on Learning Representations, 2021."},{"key":"10.1016\/j.patcog.2026.114231_b31","unstructured":"N. Gruver, M.A. Finzi, S.D. Stanton, A.G. Wilson, Deconstructing the Inductive Biases of Hamiltonian Neural Networks, in: International Conference on Learning Representations, 2022."},{"issue":"4","key":"10.1016\/j.patcog.2026.114231_b32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3730834","article-title":"Fast but accurate: A real-time hyperelastic simulator with robust frictional contact","volume":"44","author":"Zeng","year":"2025","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.patcog.2026.114231_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2023.116333","article-title":"Advanced discretization techniques for hyperelastic physics-augmented neural networks","volume":"416","author":"Franke","year":"2023","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"key":"10.1016\/j.patcog.2026.114231_b34","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110254","article-title":"HRNet: 3D object detection network for point cloud with hierarchical refinement","volume":"149","author":"Lu","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114231_b35","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110997","article-title":"Revisiting 3D point cloud analysis with Markov process","volume":"158","author":"Jiang","year":"2025","journal-title":"Pattern Recognit."},{"issue":"2","key":"10.1016\/j.patcog.2026.114231_b36","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1002\/nme.1620370205","article-title":"Element-free Galerkin methods","volume":"37","author":"Belytschko","year":"1994","journal-title":"Internat. J. Numer. Methods Engrg."},{"key":"10.1016\/j.patcog.2026.114231_b37","series-title":"Smoothed Particle Hydrodynamics: a Meshfree Particle Method","author":"Liu","year":"2003"},{"key":"10.1016\/j.patcog.2026.114231_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.jcp.2023.112270","article-title":"A new smoothed particle hydrodynamics method based on high-order moving-least-square targeted essentially non-oscillatory scheme for compressible flows","volume":"489","author":"Gao","year":"2023","journal-title":"J. Comput. Phys."},{"issue":"8","key":"10.1016\/j.patcog.2026.114231_b39","doi-asserted-by":"crossref","DOI":"10.1063\/5.0220606","article-title":"Robust solid boundary treatment for compressible smoothed particle hydrodynamics","volume":"36","author":"Villodi","year":"2024","journal-title":"Phys. Fluids"},{"key":"10.1016\/j.patcog.2026.114231_b40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.camwa.2023.07.015","article-title":"Generalized moving least squares vs. radial basis function finite difference methods for approximating surface derivatives","volume":"147","author":"Jones","year":"2023","journal-title":"Comput. Math. Appl."},{"key":"10.1016\/j.patcog.2026.114231_b41","first-page":"7462","article-title":"Implicit neural representations with periodic activation functions","volume":"33","author":"Sitzmann","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114231_b42","doi-asserted-by":"crossref","unstructured":"A. Pumarola, E. Corona, G. Pons-Moll, F. Moreno-Noguer, D-nerf: Neural radiance fields for dynamic scenes, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 10318\u201310327.","DOI":"10.1109\/CVPR46437.2021.01018"},{"key":"10.1016\/j.patcog.2026.114231_b43","first-page":"14955","article-title":"H-nerf: Neural radiance fields for rendering and temporal reconstruction of humans in motion","volume":"34","author":"Xu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114231_b44","unstructured":"X. Li, Y.-L. Qiao, P.Y. Chen, K.M. Jatavallabhula, M. Lin, C. Jiang, C. Gan, PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification, in: The Eleventh International Conference on Learning Representations, 2023."},{"key":"10.1016\/j.patcog.2026.114231_b45","doi-asserted-by":"crossref","unstructured":"Y. Kant, A. Siarohin, R.A. Guler, M. Chai, J. Ren, S. Tulyakov, I. Gilitschenski, Invertible neural skinning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 8715\u20138725.","DOI":"10.1109\/CVPR52729.2023.00842"},{"key":"10.1016\/j.patcog.2026.114231_b46","doi-asserted-by":"crossref","unstructured":"Z. Liao, V. Golyanik, M. Habermann, C. Theobalt, VINECS: video-based neural character skinning, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 1377\u20131387.","DOI":"10.1109\/CVPR52733.2024.00137"},{"key":"10.1016\/j.patcog.2026.114231_b47","article-title":"Unsupervised learning for physical interaction through video prediction","volume":"29","author":"Finn","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.patcog.2026.114231_b48","series-title":"International Conference on Machine Learning","first-page":"3078","article-title":"Model-based reinforcement learning for continuous control with posterior sampling","author":"Fan","year":"2021"},{"key":"10.1016\/j.patcog.2026.114231_b49","first-page":"11","article-title":"Gradient-regularized deep ritz networks for solving elliptic PDEs: Application to the Poisson equation","volume":"2026","author":"Al-Mahdawi","year":"2026","journal-title":"Babylon. J. Math."},{"issue":"4","key":"10.1016\/j.patcog.2026.114231_b50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3658184","article-title":"Simplicits: Mesh-free, geometry-agnostic elastic simulation","volume":"43","author":"Modi","year":"2024","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.patcog.2026.114231_b51","series-title":"ACM SIGGRAPH 2022 Courses","article-title":"Dynamic deformables: implementation and production practicalities (now with code!)","author":"Kim","year":"2022"},{"key":"10.1016\/j.patcog.2026.114231_b52","series-title":"DEM-NeRF: A neuro-symbolic method for scientific discovery through physics-informed simulation","author":"Tan","year":"2025"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326011969?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326011969?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T04:27:57Z","timestamp":1785904077000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326011969"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":52,"alternative-id":["S0031320326011969"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114231","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Elasticity-Aware Neural Hamiltonian Fields for dynamic 3D vision synthesis","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114231","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114231"}}