Conference Logo ProgramMaster Logo
Conference Tools for MS&T25: Materials Science & Technology
Login
Register as a New User
Help
Submit An Abstract
Propose A Symposium
Presenter/Author Tools
Organizer/Editor Tools

About this Abstract

Meeting MS&T25: Materials Science & Technology
Symposium Applications of Uncertainty Quantification (UQ) in Science and Engineering
Presentation Title Uncertainty Quantification via Deep Kernel Learning for Predicting Multimodal β-phase Volume Fraction from SXRD Patterns
Author(s) Ayorinde Emmanuel Olatunde, Ozan Dernek, Gabriel Ponon, Weiqi Yue, Qingzhe Guo, Amit Samantha, Donald W. Brown, Roger H. French, Pawan K. Tripathi, Anirban Mondal
On-Site Speaker (Planned) Ayorinde Emmanuel Olatunde
Abstract Scope The processes of obtaining scientific experimental data and statistical modelling are prone to significant errors, which are reducible but not avoidable. There are various approaches for quantifying these uncertainties, but the methods used in these quantifications also have their limitations. One of such limitations is finding the estimates used for prediction with standard kernels and not the exact kernel of the data set. In this work, we extend our ongoing efforts by incorporating deep kernel learning (DKL) before modelling with the Gaussian Process (GP) used in quantifying the uncertainties in the prediction of the multimodal β-phase volume fraction from synchrotron X-ray diffraction patterns obtained from Ti–6Al–4V alloy during heat treatment. DKL allows us to learn the kernel of our data, thereby modelling the GP with the learned kernel with for potential better performance. We will compare our results with those obtained with standard base kernels for benchmarking.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A case study of Bayesian parameter estimation for thermal property inference and uncertainty quantification
Leveraging Archival Additively Manufacturing Fatigue Data to Investigate the Role of Processing Porosity with Greater Precision
Representative microstructure for macro-scale property prediction using multi-scale models
Sparse grids for magneto-hydrodynamics
Uncertainty Quantification via Deep Kernel Learning for Predicting Multimodal β-phase Volume Fraction from SXRD Patterns

Questions about ProgramMaster? Contact programming@programmaster.org | TMS Privacy Policy | Accessibility Statement