Predicting Stress-Strain Curve with Confidence: Balance Between Data Minimization and Uncertainty Quantification by a Dual Bayesian Model.

ABSTRACT: Driven by polymer processing-property data, machine learning (ML) presents an efficient paradigm in predicting the stress-strain curve. However, it is generally challenged by (i) the deficiency of training data, (ii) the one-to-many issue of processing-property relationship (i.e., aleatoric uncertainty), and (iii) the unawareness of model uncertainty (i.e., epistemic uncertainty). Here, leveraging a Bayesian neural network (BNN) and a recently proposed dual-architected model for curve prediction, we introduce a dual Bayesian model that enables accurate prediction of the stress-strain curve while distinguishing between aleatoric and epistemic uncertainty at each processing condition. The model is trained using a Taguchi array dataset that minimizes the data size while maximizing the represe

SUBMITTER: Li T 

PROVIDER: S-EPMC11860118 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature