Transformers have revolutionized performance in Natural Language Processing and Vision, paving the way for their integration with Graph Neural Networks (GNNs). One key challenge in enhancing graph transformers is strengthening the discriminative power of distinguishing isomorphisms of graphs, which plays a crucial role in boosting their predictive performances. To address this challenge, we introduce 'Topology-Informed Graph Transformer (TIGT)', a novel transformer enhancing both discriminative power in detecting graph isomorphisms and the overall performance of Graph Transformers. TIGT consists of four components: A topological positional embedding layer using non-isomorphic universal covers based on cyclic subgraphs of graphs to ensure unique graph representation: A dual-path message-passing layer to explicitly encode topological characteristics throughout the encoder layers: A global attention mechanism: And a graph information layer to recalibrate channel-wise graph features for better feature representation. TIGT outperforms previous Graph Transformers in classifying synthetic dataset aimed at distinguishing isomorphism classes of graphs. Additionally, mathematical analysis and empirical evaluations highlight our model's competitive edge over state-of-the-art Graph Transformers across various benchmark datasets.
DTA-GT: Direction- and Topology-Aware Graph Transformer for Neural Network Representation Learning
Hunan University（湖南大学）；Hunan University of Science and Technology（湖南科技大学）；Chinese Academy of Sciences（中国科学院）；The Hong Kong Polytechnic University（香港理工大学）
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）
Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
Argonne National Laboratory（阿贡国家实验室）；Oak Ridge National Laboratory（橡树岭国家实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of California, Berkeley（加州大学伯克利分校）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
A Gravity-informed Spatiotemporal Transformer for Human Activity Intensity Prediction
National Engineering Research Center of Geographic Information System, China University of Geosciences (Wuhan)（中国地质大学（武汉）国家地理信息系统工程技术研究中心）；JD iCity, JD Technology（京东科技京东智能城市）
DeSyR: A decoupled symbolic recovery framework for PDEs with PINN-guided structure discovery and physics-based coefficient refinement
Chengdu University of Information Technology（成都信息工程大学）；Sichuan University（四川大学）；Hong Kong Polytechnic University（香港理工大学）；Southwest Petroleum University（西南石油大学）
PINN-Cast: Exploring the Role of Continuous-Depth NODE in Transformers and Physics Informed Loss as Soft Physical Constraints in Short-term Weather Forecasting
Multi-material Multi-physics Topology Optimization with Physics-informed Gaussian Process Priors
Department of Mechanical and Aerospace Engineering, University of California, Irvine（机械与航空航天工程系，加州大学伊维特分校）；Department of Civil and Environmental Engineering, University of California, Irvine（土木与环境工程系，加州大学伊维特分校）
A Residual Guided strategy with Generative Adversarial Networks in training Physics-Informed Transformer Networks
College of Science, China Agricultural University（中国农业大学理学院）；College of Information and Electrical Engineering, China Agricultural University（中国农业大学信息与电气工程学院）；College of Water Resources and Civil Engineering, China Agricultural University（中国农业大学水利与土木工程学院）
National Center for Supercomputing Applications, University of Illinois Urbana-Champaign（国家超级计算中心，伊利诺伊大学厄巴纳-香槟分校）；Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign（贝克曼先进科学与技术研究所，伊利诺伊大学厄巴纳-香槟分校）；The Grainger College of Engineering, Department of Aerospace Engineering, University of Illinois Urbana-Champaign（格拉inger工程学院，航空航天工程系，伊利诺伊大学厄巴纳-香槟分校）；The Grainger College of Engineering, Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign（格拉inger工程学院，机械科学与工程系，伊利诺伊大学厄巴纳-香槟分校）；The Grainger College of Engineering, Department of Civil and Environmental Engineering, University of Illinois Urbana-Champaign（格拉inger工程学院，土木与环境工程系，伊利诺伊大学厄巴纳-香槟分校）；Civil and Urban Engineering Department, New York University Abu Dhabi（纽约大学阿布扎比分校土木与城市工程系）；Department of Industrial and Manufacturing Systems Engineering, Kansas State University（工业与制造系统工程系，堪萨斯州立大学）
Physics informed Transformer-VAE for biophysical parameter estimation: PROSAIL model inversion in Sentinel-2 imagery
African Institute for Mathematical Sciences (AIMS), Accra, Ghana（非洲数学科学研究所（AIMS））；African Institute for Mathematical Sciences (AIMS), Cape Town, South Africa（非洲数学科学研究所（AIMS））
Residual-based Attention Physics-informed Neural Networks for Spatio-Temporal Ageing Assessment of Transformers Operated in Renewable Power Plants
Mondragon University（蒙dragon大学）；University of the Basque Country (UPV/EHU)（巴斯克大学）；Basque Centre for Applied Mathematics（巴斯克应用数学中心）；KTH, Royal Institute of Technology（皇家理工学院）；Ikerbasque, Basque Foundation for Science（巴斯克科学基金会）
LT-PINN: Lagrangian Topology-conscious Physics-informed Neural Network for Boundary-focused Engineering Optimization
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China（土木与环境工程系，香港理工大学，香港，中国）；Research Institute for Sustainable Urban Development (RISUD), The Hong Kong Polytechnic University, Hong Kong, China（可持续城市发展研究院（RISUD），香港理工大学，香港，中国）；Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong, China（机械工程系，香港理工大学，香港，中国）；School of Fashion and Textiles, The Hong Kong Polytechnic University, Hong Kong, China（时尚与纺织学院，香港理工大学，香港，中国）；Department of Mechanical Engineering, The University of Hong Kong, Hong Kong, China（机械工程系，香港大学，香港，中国）
Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer
Faculty of Mathematics and Natural Sciences, University of Oslo（奥斯陆大学数学与自然科学学院）
Transformers with Physics-Informed Encodings and Simulation-Based Inference for Robust Detection of Eccentric Binary Black Holes in Pulsar Timing Array Data
Department of Physics, National University of Singapore（新加坡国立大学物理系）；Department of Mathematics, National University of Singapore（新加坡国立大学数学系）
