Self-optimization wavelet-learning method for predicting nonlinear thermal conductivity of highly heterogeneous materials with randomly hierarchical configurations
DOI10.1016/j.cpc.2023.108969arXiv2307.15403OpenAlexW4387824316MaRDI QIDQ6147790
Yu-Feng Nie, Wei-Feng Gao, Hao Dong, Jiale Linghu
Publication date: 16 January 2024
Published in: Computer Physics Communications (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2307.15403
wavelet transformwavelet decompositionartificial neural networkintelligent optimization algorithmpolynomial nonlinear modelWeibull probabilistic model
Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Inhomogeneity in solid mechanics (74E05) Thermal effects in solid mechanics (74F05) Numerical methods for wavelets (65T60) Random structure in solid mechanics (74E35) Numerical and other methods in solid mechanics (74S99)
Cites Work
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- A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
- Exploring the 3D architectures of deep material network in data-driven multiscale mechanics
- A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials
- Second-Order Two-Scale Computational Method for Nonlinear Dynamic Thermo-Mechanical Problems of Composites with Cylindrical Periodicity
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