{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T15:01:18Z","timestamp":1776438078842,"version":"3.51.2"},"reference-count":123,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T00:00:00Z","timestamp":1761955200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["SWU-KQ24023"],"award-info":[{"award-number":["SWU-KQ24023"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2023CDJSKPT04"],"award-info":[{"award-number":["2023CDJSKPT04"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2021CDJSKPT05"],"award-info":[{"award-number":["2021CDJSKPT05"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2020CDJSK01WT07"],"award-info":[{"award-number":["2020CDJSK01WT07"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72274026"],"award-info":[{"award-number":["72274026"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1016\/j.neucom.2025.131262","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T01:35:20Z","timestamp":1755740120000},"page":"131262","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":14,"special_numbering":"C","title":["Physics-aware parameter diffusion for spatio-temporal continuum field modeling for governance"],"prefix":"10.1016","volume":"655","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2028-3510","authenticated-orcid":false,"given":"An","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6237-0450","authenticated-orcid":false,"given":"Sheng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Pengcheng","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Fan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Kun","family":"Wang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2025.131262_bib0005","author":"Li"},{"issue":"3","key":"10.1016\/j.neucom.2025.131262_bib0010","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1038\/s42256-021-00302-5","article-title":"Learning nonlinear operators VIA Deeponet based on the universal approximation theorem of operators","volume":"3","author":"Lu","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.neucom.2025.131262_bib0015","author":"Wu"},{"issue":"1","key":"10.1016\/j.neucom.2025.131262_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S1540-7489(02)80007-4","article-title":"Combustion dynamics and control: progress and challenges","volume":"29","author":"Candel","year":"2002","journal-title":"Proc. Combust. Inst."},{"issue":"1\u20132","key":"10.1016\/j.neucom.2025.131262_bib0025","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/S0010-2180(02)00394-2","article-title":"Combustion dynamics of turbulent swirling flames","volume":"131","author":"K\u00fclsheimer","year":"2002","journal-title":"Combust. Flame"},{"key":"10.1016\/j.neucom.2025.131262_bib0030","author":"Xiong"},{"key":"10.1016\/j.neucom.2025.131262_bib0035","author":"Wang"},{"key":"10.1016\/j.neucom.2025.131262_bib0040","author":"Xu"},{"issue":"6","key":"10.1016\/j.neucom.2025.131262_bib0045","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1016\/j.neuron.2018.08.011","article-title":"Molecular dynamics simulation for all","volume":"99","author":"Hollingsworth","year":"2018","journal-title":"Neuron"},{"key":"10.1016\/j.neucom.2025.131262_bib0050","unstructured":"T. Pfaff, M. Fortunato, A. Sanchez-Gonzalez, P.W. Battaglia, Learning mesh-based simulation with graph networks, (2021)."},{"key":"10.1016\/j.neucom.2025.131262_bib0055","doi-asserted-by":"crossref","unstructured":"Y. Shao, C.C. Loy, B. Dai, Transformer with implicit edges for particle-based physics simulation, (2022) 549\u2013564.","DOI":"10.1007\/978-3-031-19800-7_32"},{"key":"10.1016\/j.neucom.2025.131262_bib0060","author":"Wu"},{"issue":"8","key":"10.1016\/j.neucom.2025.131262_bib0065","doi-asserted-by":"crossref","first-page":"4050","DOI":"10.1109\/TKDE.2024.3363711","article-title":"Modeling spatio-temporal dynamical systems with neural discrete learning and levels-of-experts","volume":"36","author":"Wang","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2025.131262_bib0070","series-title":"International Conference on Machine Learning","first-page":"3208","article-title":"PDE-net: learning pdes from data","author":"Long","year":"2018"},{"issue":"1","key":"10.1016\/j.neucom.2025.131262_bib0075","first-page":"14","article-title":"Variational PDE models in image processing","volume":"50","author":"Chan","year":"2003","journal-title":"Not. AMS"},{"key":"10.1016\/j.neucom.2025.131262_bib0080","series-title":"Navier-Stokes Equations","author":"Constantin","year":"1988"},{"key":"10.1016\/j.neucom.2025.131262_bib0085","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1007\/s42241-020-0028-y","article-title":"Deep reinforcement learning in fluid mechanics: a promising method for both active flow control and shape optimization","volume":"32","author":"Rabault","year":"2020","journal-title":"J. Hydrodyn."},{"key":"10.1016\/j.neucom.2025.131262_bib0090","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.jcp.2013.02.028","article-title":"The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows","volume":"242","author":"Carlberg","year":"2013","journal-title":"J. Comput. Phys."},{"key":"10.1016\/j.neucom.2025.131262_bib0095","series-title":"Proceedings of the 40th International Conference on Machine Learning (ICML 2023)","first-page":"10222","article-title":"Disentangled generative models for robust prediction of system dynamics","author":"Fotiadis","year":"2023"},{"key":"10.1016\/j.neucom.2025.131262_bib0100","series-title":"International Conference on Learning Representation","article-title":"Space and time continuous physics simulation from partial observations","author":"Janny","year":"2024"},{"issue":"5","key":"10.1016\/j.neucom.2025.131262_bib0105","doi-asserted-by":"crossref","first-page":"2491","DOI":"10.1007\/s10694-020-01081-y","article-title":"Expanding the FDS simulation capabilities to fire tunnel scenarios through a novel multi-scale model","volume":"57","author":"Verda","year":"2021","journal-title":"Fire Technol."},{"issue":"6","key":"10.1016\/j.neucom.2025.131262_bib0110","first-page":"46","article-title":"Creating fluent readers","volume":"61","author":"Rasinski","year":"2004","journal-title":"Educ. Leadersh."},{"issue":"6","key":"10.1016\/j.neucom.2025.131262_bib0115","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."},{"key":"10.1016\/j.neucom.2025.131262_bib0120","first-page":"23426","article-title":"Meta-auto-decoder for solving parametric partial differential equations","volume":"35","author":"Huang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2020.113250","article-title":"PPINN: parareal physics-informed neural network for time-dependent PDES","volume":"370","author":"Meng","year":"2020","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"10.1016\/j.neucom.2025.131262_bib0130","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"18770","article-title":"Temporal attention unit: towards efficient spatiotemporal predictive learning","author":"Tan","year":"2023"},{"key":"10.1016\/j.neucom.2025.131262_bib0135","article-title":"Scalable transformer for PDE surrogate modeling","volume":"36","author":"Li","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0140","first-page":"26548","article-title":"Characterizing possible failure modes in physics-informed neural networks","volume":"34","author":"Krishnapriyan","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.neucom.2025.131262_bib0145","doi-asserted-by":"crossref","DOI":"10.1063\/5.0072969","article-title":"Physics-informed neural networks for imaging and parameter retrieval of photonic nanostructures from near-field data","volume":"7","author":"Chen","year":"2022","journal-title":"APL Photonics"},{"key":"10.1016\/j.neucom.2025.131262_bib0150","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."},{"key":"10.1016\/j.neucom.2025.131262_bib0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2022.114823","article-title":"Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems","volume":"393","author":"Yu","year":"2022","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"10.1016\/j.neucom.2025.131262_bib0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108900","article-title":"Physics-informed machine learning for reliability and systems safety applications: state of the art and challenges","volume":"230","author":"Xu","year":"2023","journal-title":"Rel. Eng. Syst. Saf."},{"issue":"7972","key":"10.1016\/j.neucom.2025.131262_bib0165","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1038\/s41586-023-06221-2","article-title":"Scientific discovery in the age of artificial intelligence","volume":"620","author":"Wang","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.neucom.2025.131262_bib0170","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.neucom.2025.131262_bib0175","author":"G\u00f6ring"},{"key":"10.1016\/j.neucom.2025.131262_bib0180","author":"Wu"},{"key":"10.1016\/j.neucom.2025.131262_bib0185","unstructured":"H. Wu, K. Wang, F. Xu, Y. Li, X. Wang, W. Wang, H. Wang, X. Luo, Spatio-temporal twins with a cache for modeling long-term system dynamics, 2024 https:\/\/openreview.net\/forum?id=aE6HazMgRz."},{"issue":"388","key":"10.1016\/j.neucom.2025.131262_bib0190","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.neucom.2025.131262_bib0195","first-page":"24924","article-title":"Choose a transformer: Fourier OR Galerkin","volume":"34","author":"Cao","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0200","first-page":"7561","article-title":"Leads: learning dynamical systems that generalize across environments","volume":"34","author":"Yin","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0205","series-title":"Conference on Lifelong Learning Agents","first-page":"335","article-title":"Continual learning of dynamical systems with competitive federated reservoir computing","author":"Bereska","year":"2022"},{"issue":"1","key":"10.1016\/j.neucom.2025.131262_bib0210","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0370-1573(02)00558-6","article-title":"Thermal conduction in classical low-dimensional lattices","volume":"377","author":"Lepri","year":"2003","journal-title":"Phys. Rep."},{"issue":"1526","key":"10.1016\/j.neucom.2025.131262_bib0215","first-page":"341","article-title":"Exact analysis of unsteady convective diffusion","volume":"316","author":"Gill","year":"1970","journal-title":"Proc. R. Soc. Lond. A Math. Phys. Sci."},{"key":"10.1016\/j.neucom.2025.131262_bib0220","article-title":"Neural discrete representation learning","volume":"30","author":"Van Den Oord","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0225","author":"Liu"},{"key":"10.1016\/j.neucom.2025.131262_bib0230","author":"Fortuin"},{"key":"10.1016\/j.neucom.2025.131262_bib0235","author":"Guibas"},{"key":"10.1016\/j.neucom.2025.131262_bib0240","series-title":"European Conference on Computer Vision","first-page":"170","article-title":"Unleashing transformers: parallel token prediction with discrete absorbing diffusion for fast high-resolution image generation from vector-quantized codes","author":"Bond-Taylor","year":"2022"},{"key":"10.1016\/j.neucom.2025.131262_bib0245","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11482","article-title":"Dynamic dual-output diffusion models","author":"Benny","year":"2022"},{"key":"10.1016\/j.neucom.2025.131262_bib0250","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11472","article-title":"Perception prioritized training of diffusion models","author":"Choi","year":"2022"},{"key":"10.1016\/j.neucom.2025.131262_bib0255","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2023.3261988","article-title":"Diffusion models in vision: a survey","author":"Croitoru","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.neucom.2025.131262_bib0260","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1038\/s43247-023-01084-x","article-title":"ChatClimate: grounding conversational AI in climate science","volume":"4","author":"Vaghefi","year":"2023","journal-title":"Commun. Earth Environ."},{"issue":"6","key":"10.1016\/j.neucom.2025.131262_bib0265","doi-asserted-by":"crossref","first-page":"2806","DOI":"10.1109\/TPAMI.2020.3045007","article-title":"A review on deep learning techniques for video prediction","volume":"44","author":"Oprea","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2025.131262_bib0270","article-title":"PatchTST-DME: frequency-aware transformer for potato price forecasting in agricultural management","author":"Zhang","year":"2025","journal-title":"Potato Res."},{"key":"10.1016\/j.neucom.2025.131262_bib0275","article-title":"Good environmental governance: Predicting PM2.5 by using Spatiotemporal Matrix Factorization generative adversarial network","volume":"10","author":"Zhang","year":"2022","journal-title":"Front. Environ. Sci."},{"key":"10.1016\/j.neucom.2025.131262_bib0280","series-title":"Advances in Neural Information Processing Systems","first-page":"2863","article-title":"Action-conditional video prediction using deep networks in Atari games","author":"Oh","year":"2015"},{"key":"10.1016\/j.neucom.2025.131262_bib0285","series-title":"International Conference on Learning Representations","article-title":"Deep multi-scale video prediction beyond mean square error","author":"Mathieu","year":"2016"},{"key":"10.1016\/j.neucom.2025.131262_bib0290","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"6165","article-title":"Deep analysis of CNN-based spatio-temporal representations for action recognition","author":"Chen","year":"2021"},{"key":"10.1016\/j.neucom.2025.131262_bib0295","series-title":"Conference on Computer Vision and Pattern Recognition","first-page":"1526","article-title":"MOCOGAN: decomposing motion and content for video generation","author":"Tulyakov","year":"2018"},{"key":"10.1016\/j.neucom.2025.131262_bib0300","series-title":"International Conference on Machine Learning","first-page":"843","article-title":"Unsupervised learning of video representations using LSTMS","author":"Srivastava","year":"2015"},{"key":"10.1016\/j.neucom.2025.131262_bib0305","series-title":"International Conference on Learning Representations","article-title":"Stochastic variational video prediction","author":"Babaeizadeh","year":"2018"},{"issue":"2","key":"10.1016\/j.neucom.2025.131262_bib0310","doi-asserted-by":"crossref","DOI":"10.1016\/j.aej.2020.12.009","article-title":"Spatiotemporal prediction of air quality based on LSTM neural network","volume":"60","author":"Seng","year":"2021","journal-title":"Alex. Eng. J."},{"issue":"2","key":"10.1016\/j.neucom.2025.131262_bib0315","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.1109\/TPAMI.2022.3165153","article-title":"PREDRNN: a recurrent neural network for spatiotemporal predictive learning","volume":"45","author":"Wang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2025.131262_bib0320","series-title":"International Conference on Learning Representations","article-title":"Scaling autoregressive video models","author":"Weissenborn","year":"2019"},{"key":"10.1016\/j.neucom.2025.131262_bib0325","series-title":"International Conference on Learning Representations","article-title":"VideoFlow: a flow-based generative model for video","author":"Kumar","year":"2020"},{"key":"10.1016\/j.neucom.2025.131262_bib0330","article-title":"ST-LLM+: graph enhanced spatio-temporal large language models for traffic prediction","author":"Liu","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"7","key":"10.1016\/j.neucom.2025.131262_bib0335","first-page":"7164","article-title":"MBA-STNet: Bayes-enhanced discriminative multi-task learning for flow prediction","volume":"35","author":"Miao","year":"2023","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2025.131262_bib0340","doi-asserted-by":"crossref","DOI":"10.1016\/j.firesaf.2020.102946","article-title":"Applying the FDS pyrolysis model to predict heat release rate in small-scale forced ventilation tunnel experiments","volume":"112","author":"Wang","year":"2020","journal-title":"Fire Saf. J."},{"issue":"1","key":"10.1016\/j.neucom.2025.131262_bib0345","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0749-596X(85)90019-1","article-title":"Components of fluent reading","volume":"24","author":"Baddeley","year":"1985","journal-title":"J. Mem. Lang."},{"key":"10.1016\/j.neucom.2025.131262_bib0350","series-title":"Multiphysics Modeling Using COMSOL\u00ae: A First Principles Approach","author":"Pryor","year":"2009"},{"key":"10.1016\/j.neucom.2025.131262_bib0355","series-title":"the Finite Element Method: Basic Concepts and Applications with MATLAB, MAPLE, and COMSOL","author":"Pepper","year":"2017"},{"key":"10.1016\/j.neucom.2025.131262_bib0360","series-title":"International Conference on Machine Learning","first-page":"12556","article-title":"GNOT: a general neural operator transformer for operator learning","author":"Hao","year":"2023"},{"key":"10.1016\/j.neucom.2025.131262_bib0365","unstructured":"Anonymous, Neural manifold operators for learning the evolution of physical dynamics, 2023 https:\/\/openreview.net\/forum?id=SQnOmOzqAM."},{"key":"10.1016\/j.neucom.2025.131262_bib0370","series-title":"Advances in Neural Information Processing Systems 31 (NeurIPS 2018)","first-page":"5339","article-title":"Generalizing to unseen domains via adversarial data augmentation","author":"Volpi","year":"2018"},{"key":"10.1016\/j.neucom.2025.131262_bib0375","doi-asserted-by":"crossref","unstructured":"L. Mansilla, R. Echeveste, D.H. Milone, E. Ferrante, Domain generalization VIA gradient surgery, (2021) 6630\u20136638.","DOI":"10.1109\/ICCV48922.2021.00656"},{"key":"10.1016\/j.neucom.2025.131262_bib0380","author":"Wu"},{"key":"10.1016\/j.neucom.2025.131262_bib0385","author":"Li"},{"key":"10.1016\/j.neucom.2025.131262_bib0390","author":"Gui"},{"key":"10.1016\/j.neucom.2025.131262_bib0395","article-title":"Diversify: a general framework for time series out-of-distribution detection and generalization","author":"Lu","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2025.131262_bib0400","series-title":"Thirty-Seventh Conference on Neural Information Processing Systems","article-title":"IDEA: an invariant perspective for efficient domain adaptive image retrieval","author":"Wang","year":"2023"},{"key":"10.1016\/j.neucom.2025.131262_bib0405","author":"Wu"},{"key":"10.1016\/j.neucom.2025.131262_bib0410","series-title":"International Conference on Machine Learning","first-page":"37765","article-title":"Discover and cure: concept-aware mitigation of spurious correlation","author":"Wu","year":"2023"},{"key":"10.1016\/j.neucom.2025.131262_bib0415","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"375","article-title":"Out-of-distribution generalization with causal invariant transformations","author":"Wang","year":"2022"},{"key":"10.1016\/j.neucom.2025.131262_bib0420","series-title":"the Twelfth International Conference on Learning Representations","article-title":"Nuwadynamics: discovering and updating in causal spatio-temporal modeling","author":"Wang","year":"2024"},{"key":"10.1016\/j.neucom.2025.131262_bib0425","first-page":"23519","article-title":"Towards a theoretical framework of out-of-distribution generalization","volume":"34","author":"Ye","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0430","series-title":"International Conference on Machine Learning","first-page":"25407","article-title":"Improving out-of-distribution robustness via selective augmentation","author":"Yao","year":"2022"},{"key":"10.1016\/j.neucom.2025.131262_bib0435","series-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","first-page":"11362","article-title":"Counterfactual active learning for out-of-distribution generalization","author":"Deng","year":"2023"},{"key":"10.1016\/j.neucom.2025.131262_bib0440","article-title":"Not all out-of-distribution data are harmful to open-set active learning","volume":"36","author":"Yang","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0445","doi-asserted-by":"crossref","unstructured":"C. Liu, S. Zhou, Q. Xu, H. Miao, C. Long, Z. Li, R. Zhao, Towards cross-modality modeling for time series analytics: a survey in the LLM era, 2025.","DOI":"10.24963\/ijcai.2025\/1173"},{"key":"10.1016\/j.neucom.2025.131262_bib0450","series-title":"Proceedings of the 25th IEEE International Conference on Mobile Data Management (MDM)","first-page":"31","article-title":"Spatial-temporal large language model for traffic prediction","author":"Liu","year":"2024"},{"key":"10.1016\/j.neucom.2025.131262_bib0455","series-title":"International Conference on Machine Learning","first-page":"2241","article-title":"Lie point symmetry data augmentation for neural PDE solvers","author":"Brandstetter","year":"2022"},{"issue":"3","key":"10.1016\/j.neucom.2025.131262_bib0460","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.buildenv.2006.03.023","article-title":"CFD boundary conditions for contaminant dispersion, heat transfer and airflow simulations around human occupants in indoor environments","volume":"43","author":"Srebric","year":"2008","journal-title":"Build. Environ."},{"issue":"2","key":"10.1016\/j.neucom.2025.131262_bib0465","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.atmosenv.2006.08.019","article-title":"CFD simulation of the atmospheric boundary layer: wall function problems","volume":"41","author":"Blocken","year":"2007","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.neucom.2025.131262_bib0470","series-title":"International Conference on Machine Learning","first-page":"12121","article-title":"Graph contrastive learning automated","author":"You","year":"2021"},{"key":"10.1016\/j.neucom.2025.131262_bib0475","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0480","series-title":"International Conference on Machine Learning","first-page":"13052","article-title":"Let invariant rationale discovery inspire graph contrastive learning","author":"Li","year":"2022"},{"key":"10.1016\/j.neucom.2025.131262_bib0485","author":"Fang"},{"key":"10.1016\/j.neucom.2025.131262_bib0490","author":"Fang"},{"key":"10.1016\/j.neucom.2025.131262_bib0495","unstructured":"H. Wu, K. Wang, F. Xu, Y. Li, X. Wang, W. Wang, H. Wang, X. Luo, Spatio-temporal twins with a cache for modeling long-term system dynamics, (2023)."},{"key":"10.1016\/j.neucom.2025.131262_bib0500","author":"Rasp"},{"key":"10.1016\/j.neucom.2025.131262_bib0505","series-title":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","first-page":"32","article-title":"Recognizing human actions: a local SVM approach","volume":"vol. 3","author":"Schuldt","year":"2004"},{"key":"10.1016\/j.neucom.2025.131262_bib0510","first-page":"1596","article-title":"PDEBench: an extensive benchmark for scientific machine learning","volume":"35","author":"Takamoto","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0515","author":"Bi"},{"key":"10.1016\/j.neucom.2025.131262_bib0520","first-page":"22009","article-title":"SEVIR: a storm event imagery dataset for deep learning applications in radar and satellite meteorology","volume":"33","author":"Veillette","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0525","article-title":"Convolutional LSTM network: a machine learning approach for precipitation nowcasting","volume":"28","author":"Shi","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0530","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"3170","article-title":"SIMVP: simpler yet better video prediction","author":"Gao","year":"2022"},{"key":"10.1016\/j.neucom.2025.131262_bib0535","doi-asserted-by":"crossref","first-page":"25390","DOI":"10.52202\/068431-1841","article-title":"Earthformer: exploring space-time transformers for earth system forecasting","volume":"35","author":"Gao","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0540","series-title":"Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"2948","article-title":"Stone: a spatio-temporal OOD learning framework kills both spatial and temporal shifts","author":"Wang","year":"2024"},{"key":"10.1016\/j.neucom.2025.131262_bib0545","author":"Rahman"},{"key":"10.1016\/j.neucom.2025.131262_bib0550","author":"Tran"},{"key":"10.1016\/j.neucom.2025.131262_bib0555","author":"Tan"},{"key":"10.1016\/j.neucom.2025.131262_bib0560","series-title":"International Conference on Learning Representations","article-title":"Fourier neural operator for parametric partial differential equations","author":"Li","year":"2021"},{"key":"10.1016\/j.neucom.2025.131262_bib0565","series-title":"Forty-First International Conference on Machine Learning","article-title":"Prometheus: out-of-distribution fluid dynamics modeling with disentangled graph ODE","author":"Wu","year":"2024"},{"key":"10.1016\/j.neucom.2025.131262_bib0570","unstructured":"J. Fang, W. Liu, A. Zhang, X. Wang, X. He, K. Wang, T.-S. Chua, On regularization for explaining graph neural networks: an information theory perspective, (2023) https:\/\/openreview.net\/forum?id=5rX7M4wa2R_."},{"issue":"5","key":"10.1016\/j.neucom.2025.131262_bib0575","doi-asserted-by":"crossref","first-page":"3388","DOI":"10.1109\/TPAMI.2023.3342184","article-title":"Brave the wind and the waves: discovering robust and generalizable graph lottery tickets","volume":"46","author":"Wang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2025.131262_bib0580","series-title":"Thirty-Seventh Conference on Neural Information Processing Systems","article-title":"Evaluating post-hoc explanations for graph neural networks via robustness analysis","author":"Fang","year":"2023"},{"key":"10.1016\/j.neucom.2025.131262_bib0585","author":"Fang"},{"key":"10.1016\/j.neucom.2025.131262_bib0590","article-title":"Deciphering spatio-temporal graph forecasting: a causal lens and treatment","volume":"36","author":"Xia","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0595","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"3603","article-title":"Maintaining the status quo: capturing invariant relations for OOD spatiotemporal learning","author":"Zhou","year":"2023"},{"key":"10.1016\/j.neucom.2025.131262_bib0600","first-page":"7103","article-title":"Mixture-of-experts with expert choice routing","volume":"35","author":"Zhou","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.131262_bib0605","series-title":"International Conference on Machine Learning","first-page":"5547","article-title":"GLAM: efficient scaling of language models with mixture-of-experts","author":"Du","year":"2022"},{"key":"10.1016\/j.neucom.2025.131262_bib0610","author":"Wang"},{"key":"10.1016\/j.neucom.2025.131262_bib0615","author":"Chen"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231225019344?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231225019344?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T05:47:25Z","timestamp":1772862445000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231225019344"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":123,"alternative-id":["S0925231225019344"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2025.131262","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Physics-aware parameter diffusion for spatio-temporal continuum field modeling for governance","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2025.131262","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"131262"}}