Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks.

ABSTRACT: With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental

SUBMITTER: Li L 

PROVIDER: S-EPMC11078957 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature