Traffic state estimation (TSE) bifurcates into two categories, model-driven and data-driven (e.g., machine learning, ML), while each suffers from either deficient physics or small data. To mitigate these limitations, recent studies introduced a hybrid paradigm, physics-informed deep learning (PIDL), which contains both model-driven and data-driven components. This paper contributes an improved version, called physics-informed deep learning with a fundamental diagram learner (PIDL+FDL), which integrates ML terms into the model-driven component to learn a functional form of a fundamental diagram (FD), i.e., a mapping from traffic density to flow or velocity. The proposed PIDL+FDL has the advantages of performing the TSE learning, model parameter identification, and FD estimation simultaneously. We demonstrate the use of PIDL+FDL to solve popular first-order and second-order traffic flow models and reconstruct the FD relation as well as model parameters that are outside the FD terms. We then evaluate the PIDL+FDL-based TSE using the Next Generation SIMulation (NGSIM) dataset. The experimental results show the superiority of the PIDL+FDL in terms of improved estimation accuracy and data efficiency over advanced baseline TSE methods, and additionally, the capacity to properly learn the unknown underlying FD relation.
Physics-informed deep operator network for traffic state estimation
The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China（交通工程重点实验室，教育部，同济大学，上海201804，中国）；College of Transportation Engineering, Tongji University, Shanghai 201804, China（交通工程学院，同济大学，上海201804，中国）
CIWI-CKT: Chaos-Informed Wave Interference Feature Fusion and Cross-City Knowledge Transfer for Traffic Flow Forecasting
Griffith University（格里菲斯大学）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；School of Information Engineering, Chang’an University（长安大学信息工程学院）
Informed along the road: roadway capacity driven graph convolution network for network-wide traffic prediction
Tandon School of Engineering, New York University（纽约大学工程学院）；State Key Laboratory of Internet of Things for Smart City, University of Macau（澳门大学物联网智能城市国家重点实验室）
Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario
Department of Building, Civil and Environmental Engineering, Concordia University（康科迪亚大学建筑、土木和环境工程系）；Urban Artificial Intelligence Laboratory, University of Florida（佛罗里达大学城市人工智能实验室）；Department of Civil, Environmental and Construction Engineering, University of Central Florida（中央佛罗里达大学土木、环境和建设工程系）
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
University of Maryland, College Park, MD 20742, United States（马里兰大学学院公园分校）；Florida Atlantic University, Boca Raton, FL 33431, United States（佛罗里达 Atlantic 大学）
Evolutionary Physics-Informed Temporal Fusion for Lane-Change Intention Prediction
Tandon School of Engineering（工程学院）；New York University（纽约大学）；Khoury College of Computer Science（计算机科学学院）；Northeastern University（东北大学）；School of Business（商学院）；Wake Forest University（威克森林大学）；Independent Researcher（独立研究者）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Carnegie Mellon University（卡内基梅隆大学）；University of Pennsylvania（宾夕法尼亚大学）；Qualcomm CDMA Technologies（高通CDMA技术）；University of Michigan（密歇根大学）
Make Graph Neural Networks Great Again: A Generic Integration Paradigm of Topology-Free Patterns for Traffic Speed Prediction
University of Macau（澳门大学）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of Chinese Academy of Sciences（中国科学院大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Arizona State University（亚利桑那州立大学）
Evaluating Temporal and Structural Anomaly Detection Paradigms for DDoS Traffic
Institute of Exact and Technological Sciences（精确与技术科学研究所）；Federal University of Viçosa（弗雷塔斯联邦大学）；Department of Informatics -- School of Engineering（信息学院 -- 工程学院）；University of Minho（明霍大学）；University of São Paulo（圣保罗大学）
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）
HypNO: A Graph-Based Neural Operator with Physics-Informed Message Passing for Hyperbolic Conservation Laws
Laboratory of Computer Science (LIX), École Polytechnique（计算机科学实验室（LIX），巴黎综合理工学院）；Department of Electrical Engineering and Computer Sciences, University of California, Berkeley（电气工程与计算机科学系，加利福尼亚大学伯克利分校）
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。
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）
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）
