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Meeting MS&T24: Materials Science & Technology
Symposium Uncertainty Quantification Applications in Materials and Engineering
Presentation Title Tasmanian Toolkit for Uncertainty Quantification
Author(s) Miroslav Stoyanov
On-Site Speaker (Planned) Miroslav Stoyanov
Abstract Scope Tasmanian is library for Uncertainty Quantification developed at Oak Ridge National Laboratory, with focus on sparse grids surrogate modeling, Bayesian inference and optimization. The library has been deployed in many applications ranging from surrogates for melt-pool shape geometry, to plasma-material interactions and magneto-hydrodynamics in plasma physics, to fracture mechanics in solids. Tasmanian offers a wide range of tools that address problems of different level of smoothness of the model outputs of interest with respect to the model inputs, from very smooth to discontinuous, and different computational complexity from a few minutes to hours or days at a cluster computer. The latest development in Tasmanian allow us to deploy our tools on exascale supercomputers, including the utilization of extreme concurrency and GPU accelerators. In this talk, we will present some of the new results with specific focus on multi-physics simulations for additive manufacturing and plasma physics.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Parametric Study of Optical Floating-Zone Crystal-Growth Furnace Through Modeling of Heat Transfer: Effect of Sample Properties and Environment Gas Pressure
Automating Engineering Design with UQ-Aware Scientific Learning
Bayesian Calibration of Cladding Creep Model Coefficients in the PAD5 Fuel Performance Code Using the Dakota Toolkit
Bayesian Protocols for High-Throughput Optimization of Kinematic Hardening Models Using Cyclic Microindentation Experiments
Introduction to Verification, Validation, and Uncertainty Quantification for Engineering Simulation
Quantification of Uncertainty in Microstructure Segmentation of Solid Oxide Cell Electrodes Using an Improved Watershed Methodology
Quantitative Analysis of Systematic Uncertainties in Empirical and Machine Learning Interatomic Potentials
Tasmanian Toolkit for Uncertainty Quantification
Uncertainty Quantification in Machine Learning Models with High-Dimensional Features and Large Sample Size
Uncertainty Quantification of Material Properties in Data-Poor Regimes Using Transfer Learning and Gaussian Process Regression
Unraveling Correlation between Interface Structure and Magnetic Properties of La1-xSrxCoO3−δ/La1-xSrxMnO3−δ Bilayers Using Neural Architecture Search and Deep Ensembles

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