2017/12/05 by Paul N. Patrone, Patrone, Paul N., Anthony J. Kearsley +3 · 1 citation
Engineering · Materials Science · #Applications (stat.AP) #Composite Material Mechanics #Computational Physics (physics.comp-ph) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning in Materials Science #Polymer crystallization and properties #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.1712.01900
openalex publication_date 2017/12/05 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28
In computational materials science, mechanical properties are typically\nextracted from simulations by means of analysis routines that seek to mimic\ntheir experimental counterparts. However, simulated data often exhibit\nuncertainties that can propagate into final predictions in unexpected ways.\nThus, modelers require data analysis tools that (i) address the problems posed\nby simulated data, and (ii) facilitate uncertainty quantification. In this\nmanuscript, we discuss three case studies in materials modeling where careful\ndata analysis can be leveraged to address specific instances of these issues.\nAs a unifying theme, we highlight the idea that attention to physical and\nmathematical constraints surrounding the generation of computational data can\nsignificantly enhance its analysis.\n