State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）
Learning complex network dynamics is fundamental to understanding, modelling and controlling real-world complex systems. There are two main problems in the task of predicting the dynamic evolution of complex networks: on the one hand, existing methods usually use simple graphs to describe the relationships in complex networks; however, this approach can only capture pairwise relationships, while there may be rich non-pairwise structured relationships in the network. First-order GNNs have difficulty in capturing dynamic non-pairwise relationships. On the other hand, theoretical prediction models lack accuracy and data-driven prediction models lack interpretability. To address the above problems, this paper proposes a higher-order network dynamics identification method for long-term dynamic prediction of complex networks. Firstly, to address the problem that traditional graph machine learning can only deal with pairwise relations, dynamic hypergraph learning is introduced to capture the higher-order non-pairwise relations among complex networks and improve the accuracy of complex network modelling. Then, a dual-driven dynamic prediction module for physical data is proposed. The Koopman operator theory is introduced to transform the nonlinear dynamical differential equations for the dynamic evolution of complex networks into linear systems for solving. Meanwhile, the physical information neural differential equation method is utilised to ensure that the dynamic evolution conforms to the physical laws. The dual-drive dynamic prediction module ensures both accuracy and interpretability of the prediction. Validated on public datasets and self-built industrial chain network datasets, the experimental results show that the method in this paper has good prediction accuracy and long-term prediction performance.
Physics-Informed Neural Network with Adaptive Clustering Learning Mechanism for Information Popularity Prediction
National Innovative Institute of Defense Technology, Academy of Military Science（国防科技创新研究院，军事科学院）；Capital Normal University（首都师范大学）；School of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学与技术学院）；Silesian University of Technology (SUT)（西里西亚理工大学）；University of Alberta（阿尔伯塔大学）；Constructor University（Constructor大学）；Research Center of Performance and Productivity Analysis, Istinye University（绩效与生产力分析研究中心，Istinye大学）
Physics-Informed Neural Networks for Complex Eigenfrequency Identification and Mode Structure Reconstruction of the Ground-State ITG Branch
School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；School of Physics and Electronic Information, Anhui Normal University（安徽师范大学物理与电子信息学院）；Institute of Plasma Physics, Chinese Academy of Sciences（中国科学院等离子体物理研究所）
Predicting Large-scale Urban Network Dynamics with Energy-informed Graph Neural Diffusion
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University（香港理工大学土木与环境工程系）；Department of Traffic Engineering and Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University（同济大学交通工程系和道路与交通工程重点实验室）
Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsies
Ataraxis AI；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Harvard Medical School（哈佛医学院）；Rutgers Cancer Institute（罗格斯癌症研究所）；Northwell Health（诺斯韦尔健康）；University of Pittsburgh（匹兹堡大学）；Magee-Womens Hospital of UPMC（UPMC麦奇妇女医院）；Medical University of Łódź（罗兹医科大学）；Shaare Zedek Medical Center（沙雷泽德克医疗中心）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；University of Chicago（芝加哥大学）；Jagiellonian University Medical College（雅盖隆大学医学院）；Fred Hutchinson Cancer Center（弗雷德·哈钦森癌症中心）；University of Washington（华盛顿大学）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Intelligible, Inc.（Intelligible公司）
InSight-R: A Framework for Risk-informed Human Failure Event Identification and Interface-Induced Risk Assessment Driven by AutoGraph
Institute of Nuclear and New Energy Technology（核能与新能源技术研究院）；Tsinghua University（清华大学）；National Key Laboratory of Human Factors Engineering（人因工程国家重点实验室）；Software Institute（软件研究所）；Chinese Academy of Sciences（中国科学院）
Pi-fusion: Physics-informed diffusion model for learning fluid dynamics
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）；University of Electronic Science and Technology of China（电子科技大学）
