Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites
Bokai Liu(Bauhaus-Universität Weimar), Timon Rabczuk(Bauhaus-Universität Weimar), Xiaoying Zhuang(Leibniz University Hannover), Weizhuo Lu(Umeå University), Thomas Olofsson(Umeå University)
Cited by 128
Related Papers
Stochastic integrated machine learning based multiscale approach for the prediction of the thermal conductivity in carbon nanotube reinforced polymeric composites
|Composites Science and Technology|2022|191
A stochastic multiscale method for the prediction of the thermal conductivity of Polymer nanocomposites through hybrid machine learning algorithms
|Composite Structures|2021|190
Stochastic full-range multiscale modeling of thermal conductivity of Polymeric carbon nanotubes composites: A machine learning approach
|Composite Structures|2022|165
Multi-scale modeling in thermal conductivity of Polyurethane incorporated with Phase Change Materials using Physics-Informed Neural Networks
|Renewable Energy|2023|165
Stochastic multiscale modeling of heat conductivity of Polymeric clay nanocomposites
|Mechanics of Materials|2019|136