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Meeting Materials Science & Technology 2019
Symposium Modeling Variability of Mechanical Behavior through ICME Techniques with Emphasis on Verification, Validation & Uncertainty Quantification
Organizer(s) Jacob D. Hochhalter, University of Utah
Michael Sangid, Purdue University
Corbett Battaile, Sandia National Laboratories
Barron Bichon, Southwest Research Institutue
Scope One goal of Integrated Computational Materials Engineering (ICME) is to combine material-scale characteristics with a fundamental basis for modeling mechanical performance of materials in engineering applications. By incorporating uncertainty quantification methods with validated material models across time- and length-scales, a tangible outcome is to alleviate exhaustive testing programs through model-based prediction of mechanical performance variability. ICME-based approaches are further motivated by applications requiring new performance and efficiency standards, where, for instance, thinned components invalidate current practices because of a more direct sensitivity to material-scale mechanisms. To realize the potential benefits of ICME-based design, current limitations in the demonstrated validity and computational tractability must be overcome. These limitations become increasingly evident once the holistic process-to-performance cycle is considered. This symposium celebrates verification, validation and uncertainty quantification techniques, which are necessary to build trust within models for incorporation into applications.

This multidisciplinary symposium will serve to provide illustration of recent advances in ICME-based research and its extension to engineering application. The motivating objective is to bring together researchers in uncertainty quantification, materials science, computational science, and data analytics methods to discuss current limitations and likely paths forward. Of particular interest are methods which serve to reduce the stochastic and material modeling space, such that uncertainty quantification methods can become computationally tractable. This symposium seeks presentations which illustrate:

- Multiscale material modeling for propagation of uncertainties
- Integrated multiphysics methods for process-to-performance modeling
- Reduced-order modeling methods for the stochastic and/or model space
- Verification and validation studies for engineering-scale application of ICME
- Emerging methods for knowledge-extraction from the data sets that underpin ICME

Abstracts Due 04/05/2019
Proceedings Plan Planned: Other

A Generalized Bayesian Random Effects Model for Assessing Parameter Uncertainty in Materials Simulation
Algorithms and Computational Tools for V&V
An Experimental Perspective on Computational Validation for Dynamic Mechanical Behavior
Data Analytics for ICME
Development and Transition of a Computational Materials Framework to Support Qualification of Additively Manufactured Components
Fatigue Crack Growth Surrogate Models from Symbolic Regression for Use during Topology Optimization
Information Fusion-based Microstructure Sensitive Materials Design
Integrated Computational Materials Engineering (ICME) Techniques to Enable a Material-Informed Digital Twin Prototype for Marine Structures
Modern Data Analytics Approach to Predict the Yield Strength of 9Cr Steels
NASA’s Engineering Predictive Practices for Durability and Damage Tolerance (D&DT) of Thin Walled Materials
Neural Network Potential for Al and Zn
Next Steps for Probabilistic Modeling of Additive Manufacturing of Titanium Alloys
Quantification of Uncertainty in Forging Process Induced Residual Stress and Associated Fatigue Life
Simulating the Impact of Process Parameters on Microstructure for the Powder Bed Fusion Process
Simulations to understand the effects of pores on the mechanical behavior of materials produced using powder bed fusion process
The Stochastic Behavior of Additive Lattices
Uncertainty Informed Decision Making in Inductive Design Exploration of Ti64
Uncertainty Quantification for Process-Structure-Property Linkages Extracted Using Materials Knowledge Systems Framework
Uncertainty Quantification in the Mechanical Response of Crystal Plasticity Simulations
UQ for Forward Material Models and Dependency on Training Data Quality and Quantity Using A Bayesian Modeling Paradigm
UQ Modeling of Structures and Materials Using the Hypercomplex Differentiation Method

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