|About this Abstract
||2017 TMS Annual Meeting & Exhibition
||Computational Methods and Experimental Approaches for Uncertainty Quantification and Propagation, Model Validation, and Stochastic Predictions
||Functional Uncertainty Quantification in Materials Modeling
||Sam Reeve, Alejandro Strachan
|On-Site Speaker (Planned)
Quantification of uncertainties with disparate origins is imperative for predictive simulations in materials science and engineering. Functional uncertainty quantification (FUQ) described in this work focuses directly on uncertainties originating from input constitutive functions as opposed to its parameters as it is typically done. These uncertainties primarily stem from approximations of the physics in a given model. Calculation of functional derivatives yields the sensitivity of output quantities of interest (QoI) to changes in the input function and, with additional possible input functions, QoIs can be directly corrected from one function to another without additional simulation. Examples of materials modeling and functions of interest showing the breadth and utility of the method are described: molecular dynamics (interatomic potentials), viscoplasticity self-consistent methods for texture evolution (grain interactions), and solidification modeling (permeability functions).