Suchergebnisse - Physics-embedded algorithm structure*

Physics-informed machine learning in prognostics and health management: State of the art and challenges: Physics-informed machine learning in prognostics and health management: state of the art and challenges

Autoren: Deng, Weikun Nguyen, Khanh T.P. Medjaher, Kamal et al.

Weitere Verfasser: Deng, Weikun Nguyen, Khanh T.P. Medjaher, Kamal et al.

Quelle: Applied Mathematical Modelling. 124:325-352

Schlagwörter: Artificial intelligence, knowledge, physics-informed machine learning, Numerical methods for partial differential equations, boundary value problems, Physics-embedded algorithm structure, [SPI.MAT] Engineering Sciences [physics]/Materials, prognostics and health management, Physics-informed machine learning, Prognostics and health management, physics-informed input space, Physics-informed input space, physics-constraint learning, physics-embedded algorithm structure, Physics-constraint learning, Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems

Dateibeschreibung: application/xml; application/pdf

Zugangs-URL: https://zbmath.org/8023859
https://doi.org/10.1016/j.apm.2023.07.011
https://hal.science/hal-04290849v1
https://doi.org/10.1016/j.apm.2023.07.011
https://hal.science/hal-04290849v1/document

Physics‐Embedded Machine Learning for Fatigue Cumulative Damage Prediction.

Autoren: Gao, Zhiyuan Jiang, Xiaomo Guo, Yifan et al.

Quelle: Fatigue & Fracture of Engineering Materials & Structures. Oct2025, Vol. 48 Issue 10, p4352-4374. 23p.

Schlagwörter: *MACHINE learning, *FATIGUE life, *STRUCTURAL mechanics, *EMPIRICAL research, *FATIGUE cracks

Physics-embedded deep learning inversion for transient electromagnetic method survey data.

Autoren: Li, Ruiyou Zhang, Yong Ju, Jiayi et al.

Quelle: Computers & Geosciences. Oct2025, Vol. 204, pN.PAG-N.PAG. 1p.

Schlagwörter: *METAHEURISTIC algorithms, *PHYSICAL laws, *ELECTRIC transients, *DEEP learning, *FIELD research

Research on Power Flow Prediction Based on Physics-Informed Graph Attention Network.

Autoren: Huang, Qiyue Wang, Yapeng Yang, Xu et al.

Quelle: Applied Sciences (2076-3417); Oct2025, Vol. 15 Issue 19, p10555, 22p

Schlagwörter: MICROGRIDS, GRAPH neural networks, ELECTRIC power production forecasting, ELECTRIC power distribution, DIMENSIONAL reduction algorithms, MATHEMATICAL optimization

An Electromagnetic Imaging Algorithm Based on Generative Adversarial Network for Limited Observation Angle.

Autoren: Yang, Chun Xia Yang, Xirui Zhang, Jian et al.

Quelle: Applied Computational Electromagnetics Society Journal; Aug2025, Vol. 40 Issue 8, p702-713, 12p

A Review: Research Progress in Bridge Structural Health Monitoring From the Perspective of AI Development.

Autoren: Tang, Yunchao Wan, Shuai Yang, Qingying et al.

Quelle: Structural Control & Health Monitoring; 10/27/2025, Vol. 2025, p1-37, 37p

Schlagwörter: ARTIFICIAL intelligence, CIVIL engineering, DEEP learning, EXPERT systems, CAUSAL artificial intelligence, BRIDGE inspection, MACHINE learning

Symplectic physics-embedded learning via Lie groups Hamiltonian formulation for serial manipulator dynamics prediction.

Autoren: Wang, Fei Chen, Liping Ding, Jianwan

Quelle: Scientific Reports; 9/26/2025, Vol. 15 Issue 1, p1-18, 18p

Schlagwörter: LIE groups, HAMILTONIAN mechanics, ROBOT control systems, ROBOT kinematics, SYMPLECTIC geometry, FEATURE selection, ARTIFICIAL neural networks

Review on Sound-Based Industrial Predictive Maintenance: From Feature Engineering to Deep Learning.

Autoren: Ye, Tongzhou Peng, Tianhao Yang, Lidong

Quelle: Mathematics (2227-7390); Jun2025, Vol. 13 Issue 11, p1724, 31p

Schlagwörter: DETECTION algorithms, WORK environment, INDUSTRIAL productivity, INDUSTRIALISM, FEATURE extraction, DEEP learning

Adaptive Transient Power Angle Control for Virtual Synchronous Generators via Physics-Embedded Reinforcement Learning.

Autoren: Gao, Jiemai Chen, Siyuan Fan, Shixiong et al.

Quelle: Electronics (2079-9292); Sep2025, Vol. 14 Issue 17, p3503, 27p

Schlagwörter: REINFORCEMENT learning, ELECTRIC power systems, ADAPTIVE control systems, DYNAMIC stability, FEEDBACK control systems, CLEAN energy, SYNCHRONOUS generators

Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel Structures.

Autoren: Qiao, Yu Grad, Marius Nonn, Aida

Quelle: Metals (2075-4701); Jul2025, Vol. 15 Issue 7, p812, 22p

Schlagwörter: CELLULAR automata, MICROSTRUCTURE, MATERIALS texture, THIN-walled structures, SELECTIVE laser melting, PHASE transitions, SOLID freeform fabrication, STAINLESS steel

A physics-embedded Transformer-CNN architecture for data-driven turbulence prediction and surrogate modeling of high-fidelity fluid dynamics.

Autoren: Ghosh, Sukanta Shukla, Vinod Kumar Singh, Amar et al.

Quelle: European Journal of Mechanics B: Fluids. Jan2026, Vol. 115, pN.PAG-N.PAG. 1p.

Schlagwörter: *TURBULENCE, *NAVIER-Stokes equations, *FLUID dynamics, *PREDICTION algorithms, *TRANSFORMER models, *REDUCED-order models, *DEEP learning

SHM for Complex Composite Aerospace Structures: A Case Study on Engine Fan Blades.

Autoren: Galanopoulos, Georgios Paunikar, Shweta Stamatelatos, Giannis et al.

Quelle: Aerospace (MDPI Publishing); Nov2025, Vol. 12 Issue 11, p963, 21p

Schlagwörter: STRUCTURAL health monitoring, AEROSPACE engineering, RESEARCH methodology, FAILURE analysis, CONDITION-based maintenance, FIBER Bragg gratings, TURBOMACHINE blades, COMPOSITE materials

Data-Driven Perovskite Design via High-Throughput Simulation and Machine Learning.

Autoren: Wang, Yidi Sun, Dan Zhao, Bei et al.

Quelle: Processes; Oct2025, Vol. 13 Issue 10, p3049, 21p

Schlagwörter: PEROVSKITE, MACHINE learning, COMPUTER simulation, OPTOELECTRONICS, PREDICTION models, MATERIALS analysis, ELECTROCATALYSIS, MATERIALS science

NMS-EACO: A Novel Multi-Strategy ACO for Mobile Robot Path Planning.

Autoren: Zhang, Chao Ma, Jing Wang, Xin et al.

Quelle: Electronics (2079-9292); Sep2025, Vol. 14 Issue 17, p3440, 21p

Schlagwörter: ROBOTIC path planning, NONHOLONOMIC constraints, OPTIMIZATION algorithms, MATHEMATICAL optimization, ANT algorithms

Distinct hydrologic response patterns and trends worldwide revealed by physics-embedded learning.

Autoren: Ji, Haoyu Song, Yalan Bindas, Tadd et al.

Quelle: Nature Communications; 10/15/2025, Vol. 16 Issue 1, p1-15, 15p

Schlagwörter: WATER management, FLOOD risk, HYDROLOGIC models, GROUNDWATER, CLIMATE sensitivity, WATER supply

Physics-Embedded Neural Networks for Enhanced Ultrasonic Brain Tomography.

Autoren: Ren, Jiahao Li, Jian Liu, Yang

Quelle: Journal of Physics: Conference Series; 2024, Vol. 2822 Issue 1, p1-14, 14p

Physics-Aware Reinforcement Learning for Flexibility Management in PV-Based Multi-Energy Microgrids Under Integrated Operational Constraints.

Autoren: Dong, Shimeng Yao, Weifeng Li, Zenghui et al.

Quelle: Energies (19961073); Oct2025, Vol. 18 Issue 20, p5465, 36p

Schlagwörter: PHOTOVOLTAIC power systems, REINFORCEMENT learning, ORGANIZATIONAL resilience, FEASIBILITY studies, INTERCONNECTED power systems, RENEWABLE energy sources

Research on Encryption Algorithm and Embedded System Optimization Strategy Based on IoT Security.

Autoren: Jing Liang

Quelle: Journal of Cyber Security & Mobility; 2025, Vol. 14 Issue 1, p229-258, 29p

Schlagwörter: DATA encryption, INTERNET of things, EMBEDDED computer systems, MICROCONTROLLERS, ALGORITHMS

Electrification and Smartification for Modern Tractors: A Review of Algorithms and Techniques.

Autoren: Zhang, Chaoxian Li, Jun Li, Chuxi et al.

Quelle: Agriculture; Basel; Sep2025, Vol. 15 Issue 18, p1943, 30p

Schlagwörter: ELECTRIFICATION, TRACTORS, ENERGY storage, GREENHOUSE gases, ENERGY consumption, TECHNOLOGICAL innovations, SUSTAINABLE agriculture, ADAPTIVE control systems

A Physics-Assisted Learning Method Based on the Improved U-Net for Reconstructing 2-D Dielectric Objects.

Autoren: Zhao, Zhangyue Yang, Chun Xia

Quelle: Progress in Electromagnetics Research B; 2025, Vol. 114, p89-98, 10p

Schlagwörter: DEEP learning, TRANSFORMER models, FEATURE extraction, IMAGING systems, ELECTROMAGNETIC wave scattering, GENERALIZATION