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Meeting 2018 TMS Annual Meeting & Exhibition
Symposium Computational Method and Experimental Approaches for Model Development and Validation, Uncertainty Quantification, and Stochastic Predictions
Presentation Title Uncertainty Quantification in Materials Strength Models Using Bayesian Inference
Author(s) David Rivera, Jason Bernstein, Katie Schmidt, Nathan Barton, Ana Kupresanin, Jeff Florando
On-Site Speaker (Planned) David Rivera
Abstract Scope The quantification of uncertainty in materials strength models plays a key role in the development and design of new materials. In this work an uncertainty quantification (UQ) methodology based on Bayesian statistics is applied to estimate the uncertainty linked to calibrating a strength model with limited experimental data. Specifically, the mechanical threshold stress (MTS) model is calibrated using Taylor anvil test data and the uncertainties in the parameters determined through the generation of their posterior distribution. The model is then applied to predict the deformation of Taylor impact experiments conducted outside the range of the data used in the calibration and the propagation of uncertainty demonstrated. Furthermore, the proposed methodology is compared with alternate UQ techniques such as perturbation methods. The results illustrate an approach to UQ which provides the materials design process with an awareness of inherent model uncertainty when making decisions based on limited experimental data.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

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Calibration of a Titanium Modified Embedded Atom Method Potential to High Temperature Behavior
Correlations of Numerical Precision in Material Properties Derived from Density Functional Theory
Development of a Semi-empirical Potential for Simulation of Ni Solutes Segregated in Ag Grain Boundaries
Dynamic Failure of High Energy Materials: Uncertainty Quantification and Stochastic Predictions
Errors of Molecular Dynamics Simulations, and Development of “Accurate” Analytical Bond Order Potentials for Al-Cu-H and Mg-H Systems
Extending the Reach of DFT to Molecular Simulations Using Neural Networks
It's a SNAP: Automated Generation of High-accuracy Interatomic Potentials Using Quantum Data
L-28: Extending the Angular-embedded Atom Method (A-EAM) Framework to an Al-Mg-Si Ternary System
Large Scale Sensitivity of Uncertain Parameters on Optimal Control Solutions: An Example in Additive Manufacturing
Lattice Thermal Conductivity: Uncertainty Quantification in First Principles Predictions and Experimental Validation
Linear Scaling, Quantum-accurate Interatomic Potentials with SNAP; Reaching those Hard-to-reach Places in Classical Molecular Dynamics
Machine Learning Based Atomistic Force Fields
Machine Learning Methods for Interatomic Potentials: Application to Boron Carbide
Machine Learnt Interatomic Potentials for Stanene and Germanene to Study Thermal Conductivity and Growth
New Advances in Semi-empirical Interatomic Potentials - the Modified Embedded Atom Method (MEAM)
Overcoming Singularities within Rate-independent Crystal Plasticity to Enable Realistic Latent Hardening
Parametrically Homogenized Models of Deformation and Failure of Metals and Alloys with Uncertainty-quantification
Property Localization: Quantifying the Uncertainty of Inferred Constitutive Models for Grain Boundaries
The Current State of Phase Field Benchmark Problems Developed by CHiMaD/NIST
The OpenKIM Testing Framework for Interatomic Potentials
The Role of Data Analysis in Uncertainty Quantification: Examples from Materials Science
Uncertainty Quantification for Additive Manufacturing Applications across Scales
Uncertainty Quantification for Solute Transport Modeling
Uncertainty Quantification in Materials Strength Models Using Bayesian Inference
Uncertainty Quantification of the Effect of Charge Noise on Silicon Quantum Dots
Utilizing Error in First-principle Lattice Constants to Discover Novel Low-dimensional Materials

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