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RESEARCH PAPER · Engineering Applications of Artificial Intelligence · 2024

A multiscale analysis-assisted two-stage reduced-order deep learning approach for effective thermal conductivity of arbitrary contrast heterogeneous materials

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A multiscale analysis-assisted two-stage reduced-order deep learning approach for effective thermal conductivity of arbitrary contrast heterogeneous materialsOriginalA wavelet-neural network method for efficient and high-accuracy prediction of thermal responses in random composite materials0Physics descriptors enhanced Bayesian learning method for permeability of random media under sparse data4Deep learning-based multiscale prediction of composite aerogels’ effective thermal conductivity12Accelerating the discovery of materials with expected thermal conductivity via a synergistic strategy of DFT and interpretable deep learning2Physics-informed neural networks for solving steady-state heat conduction inverse problems0An Efficient RVE-EMC Approach for Multiscale Equations of Random Heterogeneous Materials0From surface to volume: Deep learning-driven 3-dimensional realization of large 2-dimensional images for energy materials2Artificial intelligence-assisted reliability assessment in electronics packaging: a brief review0A wavelet-neural network method for efficient and high-accuracy prediction of thermal responses in random composite materials0

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  1. A wavelet-neural network method for efficient and high-accuracy prediction of thermal responses in random composite materials

    SSRN Electronic Journal · 2025-01-01

    Hao Dong, Yiheng Lou, Jiale Linghu, Yufeng Nie, Junzhi Cui

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  2. Physics descriptors enhanced Bayesian learning method for permeability of random media under sparse data

    Engineering Applications of Artificial Intelligence · 2025-01-06

    Hang Qi, Xiaofei Guan, Qing Chen, Zhengwu Jiang, Fang Liu, Jieqiong Zhang, Hehua Zhu

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  3. Deep learning-based multiscale prediction of composite aerogels’ effective thermal conductivity

    International Journal of Mechanical Sciences · 2025-07-29

    V.-T. Nguyen, Vu‐Hieu Nguyen, Salah Naı̈li

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  4. Accelerating the discovery of materials with expected thermal conductivity via a synergistic strategy of DFT and interpretable deep learning

    Materials Futures · 2025-09-18

    Yuxuan Zeng, Wei Cao, Yijing Zuo, Tan Peng, Yue Hou, Ling Miao, Ziyu Wang, Jing Shi

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  5. Physics-informed neural networks for solving steady-state heat conduction inverse problems

    Advances in Differential Equations and Control Processes · 2026-03-05

    Tangwei Liu, Qiong Zou, Xiaoqing Ruan, Dingding Yan, Jeevan Kafle, Zhong-Zhou Lan

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  6. An Efficient RVE-EMC Approach for Multiscale Equations of Random Heterogeneous Materials

    International Journal of Numerical Analysis and Modeling · 2026-03-11

    Yanfu Chen, Xixin Wu, Zihao Yang, Jieqiong Zhang Jieqiong Zhang, Junfeng Zhao

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  7. From surface to volume: Deep learning-driven 3-dimensional realization of large 2-dimensional images for energy materials

    Engineering Applications of Artificial Intelligence · 2026-05-23

    Kunning Tang, Ying Da Wang, Mohamed Regaieg, Georgy Borisochev, Isabelle C Jolivet, Ryan T. Armstrong, Peyman Mostaghimi

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  8. Artificial intelligence-assisted reliability assessment in electronics packaging: a brief review

    Moore and More · 2026-05-29

    Yanwei Dai, Fei Qin, Xinyue Jia, Yuqi Hao

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  9. A wavelet-neural network method for efficient and high-accuracy prediction of thermal responses in random composite materials

    International Journal of Heat and Mass Transfer · 2026-08-13

    Hao Dong, Yiheng Lou, Jiale Linghu, Yufeng Nie, Junzhi Cui

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