Uncertainty Quantification in Multivariable Regression for Material Property Prediction with Bayesian Neural Networks

arXiv:2311.02495v3 Announce Type: replace
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 the multi-scale and multi-physics nature of advanced materials, intricate interactions between numerous factors, limited availability of large curated datasets for model training, etc. Recently, Bayesian Neural Networks (BNNs) have emerged as a promising approach for …

arxiv bayesian cond-mat.mtrl-sci cs.lg material networks neural networks prediction property quantification regression type uncertainty