ArticleScientific reports2022
Convolutional neural networks for approximating electrical and thermal conductivities of Cu-CNT composites.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- In-plane thermal transport and interfacial thermal resistance in carbon/silicon carbide van der Waals heteronanotubes: a molecular dynamics study.RSC advances · 2026Article
- Data-driven multiscale design of composite biomaterials: Integrating experiments, imaging, and computational modeling for biomedical engineering.Materials today. Bio · 2026Review
- An Analytical Modeling Study on the Thermal Behavior of Copper-Carbon Nanotube Composite Through-Silicon Via (TSV).Nanomaterials (Basel, Switzerland) · 2026Article
- Artificial Neural Networks for Impact Strength Prediction of Composite Barriers.Materials (Basel, Switzerland) · 2025Article
- Characteristic analysis of epileptic brain network based on attention mechanism.Scientific reports · 2023Article
- A generalised deep learning-based surrogate model for homogenisation utilising material property encoding and physics-based bounds.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
This article explores the deep learning approach towards approximating the effective electrical and thermal conductivities of copper (Cu)-carbon nanotube (CNT) composites with CNTs aligned to the field direction. Convolutional neural networks (CNN) are trained to map the two-dimensional images of stochastic Cu-CNT networks to corresponding conductivities. The CNN model learns to estimate the Cu-CNT composite conductivities for various CNT volume fractions, interfacial electrical resistances, R
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