Suchergebnisse - deep conventional autoencoder finite element nonlinear problems

Two‐Dimensional Vehicle–Bridge Interaction Neural Operator for Digital Twin of Bridge Structures.

Autoren: Kaewnuratchadasorn, Chawit Wang, Jiaji Kim, Chul-Woo et al.

Quelle: Structural Control & Health Monitoring; 10/22/2025, Vol. 2025, p1-18, 18p

Schlagwörter: DIGITAL twin, STRUCTURAL engineering, DYNAMIC simulation, DEEP learning, STRUCTURAL analysis (Engineering), FINITE element method, REAL-time computing, FAULT diagnosis

Physics-Informed Machine Learning for Hybrid Digital Twin–Enhanced Damage Detection and Localization.

Autoren: Wang, Zixin Jahanshahi, Mohammad R. Lund, Alana et al.

Quelle: Journal of Engineering Mechanics; Dec2025, Vol. 151 Issue 12, p1-19, 19p

Schlagwörter: DIGITAL twin, STRUCTURAL health monitoring, DAMAGE models, MACHINE learning, DETECTION algorithms, FINITE element method, KNOWLEDGE transfer, DEEP learning

Enhancing pipeline integrity: a comprehensive review of deep learning-enabled finite element analysis for stress corrosion cracking prediction.

Autoren: Sarwar, Umair Mokhtar, Ainul Akmar Muhammad, Masdi et al.

Quelle: Engineering Applications of Computational Fluid Mechanics; Dec2024, Vol. 18 Issue 1, p1-22, 22p

Schlagwörter: ARTIFICIAL neural networks, STRESS corrosion cracking, RECURRENT neural networks, CONVOLUTIONAL neural networks, FINITE element method, DEEP learning, CATHODIC protection

Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space.

Autoren: Najafi Koopas, Rasoul Rezaei, Shahed Rauter, Natalie et al.

Quelle: Computational Mechanics; Apr2025, Vol. 75 Issue 4, p1377-1406, 30p

Schlagwörter: ARTIFICIAL neural networks, AUTOENCODERS, BOUNDARY value problems, ARTIFICIAL intelligence, THERMAL conductivity

Applications of Computational Mechanics Methods Combined with Machine Learning and Neural Networks: A Systematic Review (2015–2025).

Autoren: Pawlik, Lukasz Wilk-Jakubowski, Jacek Lukasz Frej, Damian et al.

Quelle: Applied Sciences (2076-3417); Oct2025, Vol. 15 Issue 19, p10816, 53p

Schlagwörter: MACHINE learning, ARTIFICIAL neural networks, COMPUTATIONAL mechanics, REDUCED-order models, UNCERTAINTY (Information theory), FINITE element method

A Distributed Sensing- and Supervised Deep Learning-Based Novel Approach for Long-Term Structural Health Assessment of Reinforced Concrete Beams.

Autoren: Jayawickrema, Minol Herath, Madhubhashitha Hettiarachchi, Nandita et al.

Quelle: Metrology; Sep2025, Vol. 4 Issue 3, p40, 29p

Schlagwörter: STRUCTURAL health monitoring, DEEP learning, ARTIFICIAL neural networks, FINITE element method, STRESS measurement (Mechanics), CONCRETE beams, FIBER optical sensors

Deep learning-based surrogate models for parametrized PDEs: Handling geometric variability through graph neural networks.

Autoren: Franco, Nicola Rares Fresca, Stefania Tombari, Filippo et al.

Quelle: Chaos; Dec2023, Vol. 33 Issue 12, p1-25, 25p

Schlagwörter: DEEP learning, PARTIAL differential equations, NONLINEAR differential equations, FINITE element method

A novel computational approach combined with deep learning for dynamic analysis of sandwich plates featuring inversely designed auxetic core.

Autoren: Fang, Xi Shen, Hui-Shen Wang, Hai

Quelle: Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences; 6/15/2025, Vol. 481 Issue 2316, p1-27, 27p

Schlagwörter: DEEP learning, DYNAMIC models, NUMERICAL analysis, MECHANICAL vibration research, COMPOSITE plates, FINITE element method

Finite Element Method-Based Modeling of a Novel Square Photonic Crystal Fiber Surface Plasmon Resonance Sensor with a Au–TiO 2 Interface and the Relevance of Artificial Intelligence Techniques in Sensor Optimization.

Autoren: Ramola, Ayushman Shakya, Amit Kumar Bergman, Arik

Quelle: Photonics; Jun2025, Vol. 12 Issue 6, p565, 25p

Schlagwörter: PHOTONIC crystal fibers, SURFACE plasmon resonance, ARTIFICIAL intelligence, MACHINE learning, FINITE element method, DEEP learning

A data-driven approach for real-time soft tissue deformation prediction using nonlinear presurgical simulations.

Autoren: Liu, Haolin Han, Ye Emerson, Daniel et al.

Quelle: PLoS ONE; 4/14/2025, Vol. 20 Issue 4, p1-18, 18p

Schlagwörter: OPERATIVE ultrasonography, MINIMALLY invasive procedures, MAGNETIC resonance imaging, ULTRASONIC imaging, FINITE element method, FIDUCIAL markers (Imaging systems)

A Semisupervised Transfer Learning Method for Structural Damage Identification and Its Application to a Cable‐Stayed Bridge.

Autoren: Lu, Naiwei Xiao, Xiangyuan Cui, Jian et al.

Quelle: Structural Control & Health Monitoring; 10/12/2025, Vol. 2025, p1-18, 18p

Schlagwörter: CABLE-stayed bridges, TRANSFER of training, CONVOLUTIONAL neural networks, FINITE element method, STRUCTURAL health monitoring, SUPERVISED learning

Accelerated Inverse Design of Multi-Parallel Microperforated Panel Absorbers via Physics-Informed Neural Networks.

Autoren: Jiang, Liyang Cao, Bohan Huang, Ao et al.

Quelle: Applied Sciences (2076-3417); Nov2025, Vol. 15 Issue 22, p11955, 15p

Schlagwörter: ABSORPTION of sound, ACOUSTICAL materials, LATENT variables, DEEP learning, MACHINE learning, ENGINEERING simulations, REVERSE engineering

Application of Machine Learning and Deep Learning in Finite Element Analysis: A Comprehensive Review.

Autoren: Nath, Dipjyoti Ankit Neog, Debanga Raj et al.

Quelle: Archives of Computational Methods in Engineering; Jul2024, Vol. 31 Issue 5, p2945-2984, 40p

Quantitative Assessment of Bolt Looseness in Beam–Column Joints Using SH-Typed Guided Waves and Deep Neural Network.

Autoren: Zhang, Ru Sui, Xiaodong Duan, Yuanfeng et al.

Quelle: Applied Sciences (2076-3417); Jun2025, Vol. 15 Issue 12, p6425, 19p

Schlagwörter: ARTIFICIAL neural networks, CONVOLUTIONAL neural networks, LONG short-term memory, DATA augmentation, FINITE element method, WAVE packets

Research on Structural Mechanics Stress and Strain Prediction Models Combining Multi-Sensor Image Fusion and Deep Learning.

Autoren: Shan, Yifeng Zhen, Mengzhe Fill, Heinz D.

Quelle: Applied Sciences (2076-3417); Apr2025, Vol. 15 Issue 7, p4067, 24p

Schlagwörter: MULTISENSOR data fusion, STRAINS & stresses (Mechanics), IMAGE fusion, STRUCTURAL mechanics, DEEP learning

Multi-view information fusion using multi-view variational autoencoder to predict proximal femoral fracture load.

Autoren: Chen Zhao Joyce H. Keyak Xuewei Cao et al.

Quelle: Frontiers in Endocrinology; 2023, p1-14, 14p

Schlagwörter: PROXIMAL femoral fractures, BONE density, FEMUR neck, HIP fractures, WHOLE genome sequencing, FINITE element method, COMPUTED tomography

Programmable piezoelectric phononic crystal beams with shunt circuits: A deep learning neural network-assisted design strategy for real-time tunable bandgaps.

Autoren: Zhang, Gongye Gao, Xingyu Hong, Jun et al.

Quelle: Journal of Applied Physics; 10/21/2024, Vol. 136 Issue 15, p1-17, 17p

Schlagwörter: PHONONIC crystals, VIBRATION isolation, ELECTRIC capacity, ALGORITHMS, DEEP learning

A Comprehensive Review on Bridging the Research Gap in AI-Driven Material Simulation for FRP Composites.

Autoren: Diniță, Alin Rosca, Cosmina-Mihaela Tănase, Maria et al.

Quelle: Computer Modeling in Engineering & Sciences (CMES); 2025, Vol. 144 Issue 1, p147-199, 53p

Schlagwörter: ARTIFICIAL intelligence, COMPOSITE materials, MECHANICAL behavior of materials, BIBLIOMETRICS, MATERIALS analysis, MATHEMATICAL optimization, FINITE element method

Horseshoe Lattice Property-Structure Inverse Design Based on Deep Learning.

Autoren: Guancen Liu Zhiwei Zheng Rusheng Zhao et al.

Quelle: Materials Transactions; 2024, Vol. 65 Issue 3, p308-317, 10p

Schlagwörter: DEEP learning, FINITE element method

Design optimization of a large industrial steel structure using an enhanced K-means clustering optimizer and finite-element model updating.

Autoren: Minh, Hoang-Le Sang-To, Thanh Le-Van, Binh et al.

Quelle: Archives of Civil & Mechanical Engineering (Elsevier Science); Jan2025, Vol. 25 Issue 1, p1-37, 37p