Determining the defect locations and sizes in elastic plates by using the artificial neural network and boundary element method
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Publication:2128041
DOI10.1016/J.ENGANABOUND.2022.03.030OpenAlexW4226108644MaRDI QIDQ2128041
Yang Yang, Xinyue Han, Yijun J. Liu
Publication date: 21 April 2022
Published in: Engineering Analysis with Boundary Elements (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.enganabound.2022.03.030
boundary element methodartificial neural networkstructural health monitoringdefect localizationregression inverse problem
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- Nondestructive elastostatic identification of unilateral cracks through BEM and neural networks
- Smart finite elements: a novel machine learning application
- SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
- Data-driven inverse modelling through neural network (deep learning) and computational heat transfer
- A new boundary element method for modeling wave propagation in functionally graded materials
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures
- Data-driven computational mechanics
- Dynamic behaviors of tapered bi-directional functionally graded beams with various boundary conditions under action of a moving harmonic load
- Neural Networks and Deep Learning
- Solving ill-posed inverse problems using iterative deep neural networks
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