Learning computational fluid dynamics (CFD) traditionally relies on computationally intensive simulations of the Navier-Stokes equations. Recently, large language models (LLMs) have shown remarkable pattern recognition and reasoning abilities in natural language processing (NLP) and computer vision (CV). However, these models struggle with the complex geometries inherent in fluid dynamics. We introduce FLUID-LLM, a novel framework combining pre-trained LLMs with spatiotemporal-aware encoding to predict unsteady fluid dynamics. Our approach leverages the temporal autoregressive abilities of LLMs alongside spatial-aware layers, bridging the gap between previous CFD prediction methods. Evaluations on standard benchmarks reveal significant performance improvements across various fluid datasets. Our results demonstrate that FLUID-LLM effectively integrates spatiotemporal information into pre-trained LLMs, enhancing CFD task performance.
STAMPsy: Towards SpatioTemporal-Aware Mixed-Type Dialogues for Psychological Counseling
Peking University（北京大学）；Beihang University（北京航空航天大学）；Beijing Jiaotong University（北京交通大学）；East China Normal University（华东师范大学）；Xuanwu Hospital Capital Medical University（首都医科大学宣武医院）；Peking University Sixth Hospital（北京大学第六医院）
A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems
Department of Applied and Computational Mathematics and Statistics, University of University of Notre Dame（应用与计算数学与统计学系，诺特大学）；Department of Environmental and Civil Engineering, University of Notre Dame（环境与土木工程系，诺特大学）
University of Montpellier（蒙彼利埃大学）；Inria（法国国家信息与自动化技术研究所）；CNRS（法国国家科学研究中心）；LIRMM（蒙彼利埃信息与多媒体研究所）；Laboratoire des Sciences du Climat et de l’Environnement（气候与环境科学实验室）；UMR 8212 CEA-CNRS-UVSQ（CEA-CNRS-UVSQ联合实验室）；Université Paris-Saclay（巴黎-萨克雷大学）；IPSL（国际行星科学实验室）；London Mathematical Laboratory（伦敦数学实验室）；LMD/IPSL；ENS（法兰西公学）；Université PSL（巴黎政治学院）；École Polytechnique（巴黎高等理工学院）；Institut Polytechnique de Paris（巴黎理工学院）；Sorbonne Université（索邦大学）
Event-Aware Spatiotemporal Precipitation Forecasting with Geographic Context and Physics-Guided Regularization
Beijing Institute of Technology（北京理工大学）；Tangshan Research Institute, Beijing Institute of Technology（北京理工大学唐山研究院）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
Physically consistent and uncertainty-aware learning of spatiotemporal dynamics
Technical University of Munich（慕尼黑技术大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；University of Bristol（布里斯托大学）；Universitat de València（瓦伦西亚大学）；Munich Climate Center（慕尼黑气候中心）
Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes
Department of Geophysics, Stanford University（地质系，斯坦福大学）；Department of Earth and Planetary Science, University of California Berkeley（地球与行星科学系，加州大学伯克利分校）；Energy Geoscience Division, Lawrence Berkeley National Laboratory（能源地球科学部，伯克利国家实验室）；Department of Energy Science and Engineering, Stanford University（能源科学与工程系，斯坦福大学）
Role-Aware Conditional Inference for Spatiotemporal Ecosystem Carbon Flux Prediction
University of Pittsburgh（匹兹堡大学）；University of Alabama（阿拉巴马大学）；Purdue University（普渡大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；NOAA Global Monitoring Laboratory（国家海洋数据中心全球监测实验室）；University of Maryland（马里兰大学）
FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing
Department of Information Systems University of Maryland, Baltimore County（信息系统系马里兰大学巴尔的摩分校）；Department of Statistics Jagannath University（统计系贾甘纳思大学）；MBBS Shaheed M. Monsur Ali Medical College（谢赫莫纳斯尔阿里医学院）
G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
School of Data Science, University of Virginia（数据科学学院，弗吉尼亚大学）；University of Virginia（弗吉尼亚大学）；Department of Engineering Systems and Environment, University of Virginia（工程系统与环境系，弗吉尼亚大学）；Department of Mechanical and Aerospace Engineering, University of Virginia（机械与航空航天工程系，弗吉尼亚大学）；University of Iowa（爱荷华大学）
Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study
Massachusetts Institute of Technology（麻省理工学院）；Singapore–MIT Alliance for Research and Technology Centre (SMART)（新加坡-麻省理工研究与技术联盟中心）；University of Florida（佛罗里达大学）
PARCv2: Physics-aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics Modeling
School of Data Science（数据科学学院）；University of Virginia（弗吉尼亚大学）；Department of Astronomy（天文学系）；Department of Mechanical Engineering（机械工程系）；University of Iowa（爱荷华大学）；School of Mechanical Engineering（机械工程学院）；Kyungpook National University（庆北国立大学）；Department of Mechanical and Aerospace Engineering（机械与航空航天工程系）
Decision-aware training of spatiotemporal forecasting models to select a top K subset of sites for intervention
Department of Computer Science, Tufts University, Medford, Massachusetts, United States（计算机科学系，塔夫茨大学，马萨诸塞州梅德福，美国）；Department of Public Health and Community Medicine, Tufts University School of Medicine, Boston, Massachusetts, United States（公共卫生与社区医学系，塔夫茨大学医学院，马萨诸塞州波士顿，美国）
