Explainable machine learning for multiscale thermal conductivity modeling in polymer nanocomposites with uncertainty quantification

Bokai Liu; Pengju Liu; Yizheng Wang; Zhenkun Li; Hongqing Lv; Weizhuo Lu; Thomas Olofsson; Timon Rabczuk

doi:10.1016/j.compstruct.2025.119292

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Explainable machine learning for multiscale thermal conductivity modeling in polymer nanocomposites with uncertainty quantification

Bokai Liu Pengju Liu Yizheng Wang Zhenkun Li Hongqing Lv Weizhuo Lu Thomas Olofsson Timon Rabczuk

Year: 2025 Journal: Composite Structures Vol: 370 Pages: 119292-119292 Publisher: Elsevier BV

DOI: 10.1016/j.compstruct.2025.119292

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Keywords:

Thermal conductivity Nanocomposite Uncertainty quantification Materials science Polymer nanocomposite Polymer Composite material Multiscale modeling Thermal Conductivity Uncertainty analysis Artificial intelligence Computer science Machine learning Physics Thermodynamics Simulation Chemistry

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Machine Learning in Materials Science

Physical Sciences → Materials Science → Materials Chemistry

Thermal properties of materials

Physical Sciences → Materials Science → Materials Chemistry

Probabilistic and Robust Engineering Design

Social Sciences → Decision Sciences → Statistics, Probability and Uncertainty

Explainable machine learning for multiscale thermal conductivity modeling in polymer nanocomposites with uncertainty quantification

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