The Role of Data Analysis in Uncertainty Quantification: Case Studies for Materials Modeling

TL;DR

This paper discusses how careful data analysis, considering physical and mathematical constraints, improves uncertainty quantification in computational materials science through three case studies.

Contribution

It introduces a framework emphasizing the importance of physical and mathematical constraints in data analysis for uncertainty quantification in materials modeling.

Findings

Enhanced accuracy in property predictions through constraint-aware analysis

Identification of key sources of uncertainty in simulated data

Improved robustness of computational models with tailored data analysis

Abstract

In computational materials science, mechanical properties are typically extracted from simulations by means of analysis routines that seek to mimic their experimental counterparts. However, simulated data often exhibit uncertainties that can propagate into final predictions in unexpected ways. Thus, modelers require data analysis tools that (i) address the problems posed by simulated data, and (ii) facilitate uncertainty quantification. In this manuscript, we discuss three case studies in materials modeling where careful data analysis can be leveraged to address specific instances of these issues. As a unifying theme, we highlight the idea that attention to physical and mathematical constraints surrounding the generation of computational data can significantly enhance its analysis.