Artificial intelligence meets flexible sensors: emerging smart flexible sensing systems driven by machine learning and artificial synapses

T Sun, B Feng, J Huo, Y Xiao, W Wang, J Peng, Z Li… - Nano-Micro Letters, 2024 - Springer
The recent wave of the artificial intelligence (AI) revolution has aroused unprecedented
interest in the intelligentialize of human society. As an essential component that bridges the …

Heat transfer enhancement for 3D chip thermal simulation and prediction

C Wang, K Vafai - Applied Thermal Engineering, 2024 - Elsevier
Parameter changes in the complex internal structure of multi-layer 3D stacked chips will
greatly reduce the efficiency of modeling and thermal analysis. In this work, by combining …

An interpretable deep learning strategy for effective thermal conductivity prediction of porous materials

Q Huang, D Hong, B Niu, D Long, Y Zhang - International Journal of Heat …, 2024 - Elsevier
The effective thermal conductivity of porous materials plays a pivotal role in advancing novel
thermal insulation materials. Existing theoretical prediction methods are computationally …

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

Z Yang, X Wu, X He, X Guan - Engineering Applications of Artificial …, 2024 - Elsevier
Effective thermal conductivity (ETC) is an important property of heterogeneous materials in
many thermal management applications. Recently, there is increasing interest to establish …

Deep learning assisted prediction on main factors influencing shear strength of sintered nano Ag-Al joints under high temperature aging

L Zhao, Y Dai, F Qin - Engineering Failure Analysis, 2025 - Elsevier
Bonding strength of sintered nano silver (Ag) joints has been an important index for
evaluating the reliability of power module packages, which has been reported to be …

Numerical and experimental study on anisotropic heat transfer behaviors of quartz fabric composite preforms: Multiple micro‐scale models method

H Wu, X Wang, H Peng, X Ding, P Du, Z Han… - Polymer …, 2024 - Wiley Online Library
This paper presents a comprehensive study on anisotropic heat transmission behaviors in
quartz fiber fabrics. Considering the random distribution characteristic of fibers within the …

Thermal conductivity prediction of Al2O3-doped tetragonal YSZ coatings using deep learning

Q Chen, S Han, X Song, Y Zeng, Y Han - Journal of the European Ceramic …, 2024 - Elsevier
Thermal barrier coatings, including Al 2 O 3-doped tetragonal yttria-stabilized zirconia (t-
YSZ) coatings are vital in diverse applications. Thermal conductivity is a key property, but …

Generative AI-enabled microstructure design of porous thermal interface materials with desired effective thermal conductivity

C Du, G Zou, J Huo, B Feng, L Liu - Journal of Materials Science, 2023 - Springer
The conventional approach to achieve desired effective thermal conductivity (ETC) of porous
thermal interface materials (TIM) is processing-microstructure-properties forward analysis …

Deep image learning of quantitative structure-property relationships of cooper alloys via feature augmentation on Geodesic curve in shape space

Y Han, G Wan, B Wang, Y Liu - arXiv preprint arXiv:2404.09515, 2024 - arxiv.org
Understanding how the structure of materials affects their properties is a cornerstone of
materials science and engineering. However, traditional methods have struggled to …

Conductivity and Hardness Prediction of Cu-Cr-Zr via Feature Augmentation on Geodesic Curve

Y Han, G Wan, Y Liu, B Wang - 2024 5th International Seminar …, 2024 - ieeexplore.ieee.org
Cu-Cr-Zr alloy has garnered significant attention for its outstanding performance in high-
temperature and high-radiation environments, such as nuclear fusion reactors. However …