Car-following behavior has been extensively studied using physics-based models, such as the Intelligent Driver Model. These models successfully interpret traffic phenomena observed in the real-world but may not fully capture the complex cognitive process of driving. Deep learning models, on the other hand, have demonstrated their power in capturing observed traffic phenomena but require a large amount of driving data to train. This paper aims to develop a family of neural network based car-following models that are informed by physics-based models, which leverage the advantage of both physics-based (being data-efficient and interpretable) and deep learning based (being generalizable) models. We design physics-informed deep learning car-following (PIDL-CF) architectures encoded with two popular physics-based models - IDM and OVM, on which acceleration is predicted for four traffic regimes: acceleration, deceleration, cruising, and emergency braking. Two types of PIDL-CFM problems are studied, one to predict acceleration only and the other to jointly predict acceleration and discover model parameters. We also demonstrate the superior performance of PIDL with the Next Generation SIMulation (NGSIM) dataset over baselines, especially when the training data is sparse. The results demonstrate the superior performance of neural networks informed by physics over those without. The developed PIDL-CF framework holds the potential for system identification of driving models and for the development of driving-based controls for automated vehicles.
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California（明希斯电气与计算机工程系，南加州大学）；Alfred E. Mann Department of Biomedical Engineering, University of Southern California（阿尔弗雷德·E·曼生物医学工程系，南加州大学）；Thomas Lord Department of Computer Science, University of Southern California（托马斯·劳德计算机科学系，南加州大学）
Physics-informed network paradigm with data generation and background noise removal for diverse distributed acoustic sensing applications
State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronic Engineering, Shanghai Jiao Tong University（先进光通信系统与网络国家重点实验室，电子工程学院，上海交通大学）
ProtCLIP: Function-Informed Protein Multi-Modal Learning
Zhejiang University（浙江大学）；The Second Affiliated Hospital, School of Medicine, Zhejiang University（浙江大学医学院附属第二医院）；Alibaba Cloud Computing（阿里云云计算）；University of Science and Technology of China（中国科学技术大学）；HKUST(Guangzhou)（香港科技大学（广州））
ProtCLIP 通过构建 ProtAnno 数据集和功能引导的分段预训练范式，学习功能感知的蛋白质多模态表示，在 22 个蛋白质基准上取得 SOTA 性能。
Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction
Institute of High Performance Computing（高性能计算研究所）；Centre for Frontier AI Research（前沿人工智能研究中心）；School of Mechanical Engineering（机械工程学院）；College of Computing and Data Science（计算与数据科学学院）
Physics-Informed Extreme Learning Machine (PIELM): Opportunities and Challenges
Shandong University（山东大学）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；University of Naples Federico II（那不勒斯费德里科二世大学）；University of Leeds（利兹大学）；Shandong Institute of Mechanical Design and Research（山东省机械设计研究院）
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
College of Aerospace Science and Engineering, National University of Defense Technology（国防科技大学航空航天学院）；Defense Innovation Institute, Chinese Academy of Military Science（中国人民解放军军事科学院国防创新研究院）
Rethinking Input Domains in Physics-Informed Neural Networks via Geometric Compactification Mappings
School of Information and Software Engineering, University of Electronic Science and Technology of China（信息与软件工程学院，电子科学与技术大学）；Computer Science and Engineering, University of Electronic Science and Technology of China（计算机科学与工程，电子科学与技术大学）；Institute of Software Chinese Academy of Sciences, Beijing, China（软件研究所，中国科学院北京）；School of Robotics and Advanced Manufacture, Harbin Institute of Technology, Shenzhen, China（机器人与先进制造学院，哈尔滨工业大学深圳）
Physics-Informed Neural Operators for Cardiac Electrophysiology
Department of Informatics, King’s College London, UK（信息学院，伦敦国王学院，英国）；Research Department of Digital Twins in Healthcare, King’s College London, UK（医疗数字孪生研究部，伦敦国王学院，英国）
AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling
SVKM’s Dwarkadas J. Sanghvi College of Engineering（SVKM德瓦卡达斯·J·桑格维工程学院）；New York University Abu Dhabi (NYUAD)（纽约大学阿布扎比分校）；NYUAD Research Institute（纽约大学阿布扎比分校研究院）；The Maersk Mc-Kinney Moller Institute（马士基麦克-金尼·莫勒研究所）；University of Southern Denmark（南丹麦大学）
A Two-Stage Imaging Framework Combining CNN and Physics-Informed Neural Networks for Full-Inverse Tomography: A Case Study in Electrical Impedance Tomography (EIT)
Institute of Intelligent Machines, Hefei Institute of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院智能机械研究所）；University of Science and Technology of China（中国科学技术大学）
Physics-Informed Generative Solver: Bridging Data-Driven Priors and Conservation Laws for Stable Spatiotemporal Field Reconstruction
School of Advanced Manufacturing Engineering, Nanjing University（南京大学先进制造工程学院）；National Laboratory of Solid State Microstructures, Nanjing University（南京大学固态微结构国家实验室）；Suzhou Acoustics Industry Technology Research Institute Co., Ltd.（苏州声学工业技术研究所有限公司）；School of Mechanical and Electric Engineering, Soochow University（苏州大学机械与电子工程学院）；Shishan Laboratory, Nanjing University（仙山实验室）
Unsupervised Physics-Informed Operator Learning through Multi-Stage Curriculum Training
organization= Department of Civil；organization= QC Ware Corp. , city= Paris , country= France；organization= Department of Computer Science, KTH Royal Institute of Technology , city= Stockholm , country= Sweden；organization= IRIF, CNRS \& Université Paris Cité , city= Paris , country= France
PUNCH: Physics-informed Uncertainty-aware Network for Coronary Hemodynamics
AngioInsight Inc.（AngioInsight公司）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Michigan（密歇根大学）；Cardiovascular Research Foundation（心血管研究基金会）；University of California, Riverside（加州大学河滨分校）
Physics-informed graph neural networks for flow field estimation in carotid arteries
organization= University of Twente , city= Enschede , country= The Netherlands；organization= Amsterdam University Medical Center , city= Amsterdam , country= The Netherlands；organization= Research Institute Amsterdam Cardiovascular Sciences, Diabetes \& Metabolism , city= Amsterdam , country= The Netherlands
Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps
Pontificia Universidad Católica de Chile（智利天主教大学）；University of Graz（格拉茨大学）；Medical University of Graz（格拉茨医科大学）；University of Pennsylvania（宾夕法尼亚大学）；Università della Svizzera italiana（瑞士意大利语区大学）
WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks
Millennium Institute for Intelligent Healthcare Engineering, iHEALTH；Department of Mechanical；Metallurgical Engineering, School of Engineering, Pontificia Universidad Cat\'olica de Chile, Santiago, Chile；Institute for Biological；Medical Engineering, Schools of Engineering, Medicine
Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care
Tsinghua University（清华大学）；Institute of Brain and Cognitive Sciences, Tsinghua University（清华大学脑与认知科学研究院）；Chinese PLA General Hospital（中国人民解放军总医院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Physics-informed self-supervised deep learning reconstruction for accelerated first-pass perfusion cardiac MRI
Universidad de Valladolid（巴利亚多利德大学）；King’s College London（伦敦国王学院）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Centre for Marine Sciences - CCMAR（海洋科学中心（CCMAR））
Learn and Verify: A Framework for Rigorous Verification of Physics-Informed Neural Networks
Global Center for Science and Engineering（全球科学与工程中心）；Waseda University（早稻田大学）；Faculty of Engineering（工学院）；Tokyo University of Agriculture and Technology（东京农业大学）
