This paper introduces a novel physics-informed impact identification (Phy-ID) framework. The proposed method integrates observational, inductive, and learning biases to combine physical knowledge with data-driven inference in a unified modelling strategy, achieving physically consistent and numerically stable impact identification. The physics-informed approach structures the input space using physics-based energy indicators, constrains admissible solutions via architectural design, and enforces governing relations via hybrid loss formulations. Together, these mechanisms limit non-physical solutions and stabilise inference under degraded measurement conditions. A disjoint inference formulation is used as a representative use case to demonstrate the framework capabilities, in which impact velocity and impactor mass are inferred through decoupled surrogate models, and impact energy is computed by enforcing kinetic energy consistency. Experimental evaluations show mean absolute percentage errors below 8% for inferred impact velocity and impactor mass and below 10% for impact energy. Additional analyses confirm stable performance under reduced data availability and increased measurement noise, as well as generalisation for out-of-distribution cases across pristine and damaged regimes when damaged responses are included in training. These results indicate that the systematic integration of physics-informed biases enables reliable, physically consistent, and data-efficient impact identification, highlighting the potential of the approach for practical monitoring systems.
An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study
Defining Energy Indicators for Impact Identification on Aerospace Composites: A Structured Feature Selection Approach Guided by Domain Knowledge
Department of Mechanics of Solids, Surfaces and Systems, Dynamics Based Maintenance (DBM) Group, University of Twente（固体力学、表面与系统部门，基于动态的维护（DBM）小组，特文特大学）；Department of Aerospace Vehicles Integrity and Life Cycle Support (AVIL), Royal Netherlands Aerospace Centre（航空器完整性与生命周期支持（AVIL）部门，荷兰皇家航空研究中心）
Transformer-based Multisensor Data Fusion of Ultrasonic Guided Wave and FBG-based Strain Measurements for Multitask Aerospace Structural Health Monitoring
Physics-Informed Neural Network Modeling of Biodegradable Contaminant Transport through GCL/SL Composite Liners
Department of Civil, Environmental, and Infrastructure Engineering, George Mason University（乔治·马歇尔大学土木、环境与基础设施工程系）；State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences（中国科学院寒区工程与冻土科学联合实验室，西北生态环境资源研究院）；Laboratoire Navier/CERMES, École Nationale des Ponts et Chaussées, Institut Polytechnique de Paris（巴黎理工学院劳达实验室/塞姆斯实验室，法国国家桥梁与道路学院）
Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials
Universidad Politécnica de Madrid（马德里理工大学）；University of Oxford（牛津大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Florida（佛罗里达大学）
LFR-PINO: A Layered Fourier Reduced Physics-Informed Neural Operator for Parametric PDEs
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China（上海交通大学航空宇航科学与技术学院）；School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, China（浙江大学航空宇航科学与技术学院）；Key Laboratory for Satellite Digitalization Technology, Innovation Academy for Microsatellites of CAS, Shanghai, China（中国科学院卫星数字化技术重点实验室）
Droplet-LNO: Physics-Informed Laplace Neural Operators for Accurate Prediction of Droplet Spreading Dynamics on Complex Surfaces
Department of Mechanical Engineering, IIT Kharagpur（印度克里希纳加尔帕大学机械工程系）；Department of Computer Science & Engineering, IIT Kharagpur（印度克里希纳加尔帕大学计算机科学与工程系）
Physics-Informed Machine Learning for Characterizing System Stability
School of Aerospace Engineering（航空航天工程学院）；AIAA Student Member（美国航空航天学会学生会员）；Schools of Aerospace Engineering & Computational Science and Engineering（航空航天工程与计算科学与工程学院）；AIAA Member（美国航空航天学会会员）
Multifidelity Deep Operator Networks For Data-Driven and Physics-Informed Problems
Pacific Northwest National Laboratory（太平洋西北国家实验室）；Sandia National Laboratories（桑迪亚国家实验室）；Division of Applied Mathematics and School of Engineering, Brown University（布朗大学应用数学系与工程学院）
A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
organization= Department of Mechanical Engineering, University of Utah , addressline= , city= Salt Lake City , postcode= 84112 , state= UT , country= USA；organization= Kahlert School of Computing, University of Utah , city= Salt Lake City , postcode= 84112 , state= UT , country= USA；organization= Scientific Computing \& Imaging Institute, University of Utah , addressline= , city= Salt Lake City , postcode= 84112 , state= UT , country= USA
Integrating Newton's Laws with deep learning for enhanced physics-informed compound flood modelling
Center for Complex Hydrosystems Research（复杂水系统研究中心）；Department of Civil, Construction and Environmental Engineering（土木、建设与环境工程系）；The University of Alabama（阿拉巴马大学）
Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective
Institution for Foundation of Data Science, Yale University（数据科学基础研究所，耶鲁大学）；Penn Institute for Computational Science, University of Pennsylvania（宾夕法尼亚大学计算科学研究所）；Department of Mathematics, City University of Hong Kong（香港城市大学数学系）；Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania（宾夕法尼亚大学机械工程与应用力学系）
Adaptive Hard-Soft Physics-Informed Neural Networks for Robust Boundary-Constrained PDE Solving
College of Engineering and Computer Science, VinUniversity（VinUniversity工程与计算机科学学院）；Center for AI Research, VinUniversity（VinUniversity人工智能研究中心）；Group of Materials and Structures, School of Mechanical Engineering, Hanoi University of Science and Technology（河内科技大学机械工程学院材料与结构组）；Department of Mathematics and Informatics, Hanoi University of Science and Technology（河内科技大学数学与信息学系）
Identifying Memory Effects in Epidemics via a Fractional SEIRD Model and Physics-Informed Neural Networks
University of Wuppertal, Applied and Computational Mathematics（乌尔姆大学应用与计算数学系）；Moulay Ismail University of Meknes, FST Errachidia, Department of Mathematics, AMNEA Group（梅基斯穆拉伊大学，Errachidia 工程技术学院，数学系，AMNEA 团队）
Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle
Campus de Engenharias e Ciências Agrárias (CECA), Universidade Federal de Alagoas (UFAL), Rio Largo, Alagoas, 57100-000, Brazil（工程与农业科学学院（CECA），阿拉加斯联邦大学（UFAL），里奥拉戈，阿拉加斯，57100-000，巴西）
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers
Department of Engineering Structures（工程结构系）；TU Delft（代尔夫特理工大学）；Department of Electrical Engineering（电气工程系）；TU Eindhoven（埃因霍温理工大学）；Laboratory of Earthquake Engineering（地震工程实验室）；National Technical University of Athens（雅典国家技术大学）；Machine Learning Research Group（机器学习研究组）；University of Oxford（牛津大学）
A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method
Technical University of Darmstadt（达姆施塔特工业大学）；RWTH Aachen University（亚琛工业大学）
PINNfluence: Interpreting PINNs through Influence Functions
Technical University of Munich（慕尼黑技术大学）；Max Planck Institute for Intelligent Systems（智能系统马克斯·普朗克研究所）；University of Tübingen（图宾根大学）；ETH Zurich（苏黎世联邦理工学院）
Advancing multi-site emission control: A physics-informed transfer learning framework with mixture of experts for carbon-pollutant synergy
College of Mechanical Engineering（机械工程学院）；Alibaba Group（阿里巴巴集团）；School of Computer Science and Technology（计算机科学与技术学院）；Science and Education Integration College of Energy and Carbon Neutralization（能源与碳中和科学教育融合学院）；State Key Laboratory of Clean Energy Utilization（清洁能源利用国家重点实验室）
