gaussian process regression 1metamaterials 1reliability-based design optimization 1structure-property mapping 1uncertainty quantification 1
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physics.comp-ph2026
Uncertainty-Aware Structure-Property Mapping of Spinodoid Metamaterials via Heteroscedastic Gaussian Process Regression
Minwoo Park, Junseo Park, Mingyu Lee +4
The paper introduces a framework that uses heteroscedastic Gaussian process regression to model the uncertainty in the relationship between spinodoid metamaterial structures and th…
physics.comp-ph2025
Thermal Conductivity Estimation of Thermoelectric Materials with Uncertainty Quantification Using Bayesian Physics-Informed Neural Networks
Hyeonbin Moon, Hanbin Cho, Wabi Demeke +2
Characterizing the temperature-dependent thermal conductivity is challenging because the property varies strongly with temperature and reliable heat flow measurement, not just temp…
physics.comp-ph2025
Physics-Informed Neural Network-Based Discovery of Hyperelastic Constitutive Models from Extremely Scarce Data
Hyeonbin Moon, Donggeun Park, Hanbin Cho +3
The discovery of constitutive models for hyperelastic materials is essential yet challenging due to their nonlinear behavior and the limited availability of experimental data. Trad…