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Meeting MS&T22: Materials Science & Technology
Symposium Uncertainty Quantification in Data-Driven Materials and Process Design
Presentation Title Enabling the Fourth Paradigm of Multiscale ICME Models through Versatile Gaussian Process and Bayesian Optimization
Author(s) Anh Tran
On-Site Speaker (Planned) Anh Tran
Abstract Scope Gaussian process (GP), as well as Bayesian optimization (BO), has been a cornerstone of Bayesian machine learning methods with naturally enabled uncertainty quantification. In the first half of the talk, we will discuss a generic and versatile BO approach to tackle a handful of general optimization problems, including known and unknown constraints, multi-objective, multi-fidelity, mixed-integer, parallelization on high-performance computers, Big Data, and high-dimensional problems. In the second half of the talk, we will discuss the applications of GP/BO to several ICME models, in the materials design under uncertainty context and in the spirit of the Material Genome Initiative (2011). In particular, using ICME applications as forward models in the process-structure-property relationship, we will discuss how GP/BO fits in as an enabler to the data-driven fourth paradigm for materials design using multiple ICME models, including density functional theory, molecular dynamics, kinetic Monte Carlo, and crystal plasticity finite element.
Keywords Ceramics,

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Feature-rich Approach to the Characterization of High Temperature, Sulfate-induced Corrosion of Advanced Alloys
Active Learning for Density Functional Theory Simulations with DeepHyper
Anisotropic Creep Modeling and Uncertainty Quantification of an Electron Beam Melted AM Ni-Based Superalloy
Bayesian Calibrated Yield Strength Model for High-entropy Alloys
Bayesian Estimation and Active Learning of Data-driven Interatomic Potentials for Propagation of Uncertainty through Molecular Dynamics
Data-driven Modeling and Control for Temperature-controlled Shear Assisted Processing and Extrusion (ShAPE) using Koopman Operators
Data-driven Structure-property Mapping in Small Data Regime: Towards Increasing Generalizability
Efficient Phase Diagram Determination via Sequential Learning
Enabling the Fourth Paradigm of Multiscale ICME Models through Versatile Gaussian Process and Bayesian Optimization
Learning from Multi-source Scarce Data via Latent Map Gaussian Processes
Machine Learning of Phase Diagrams
Neural Network Surrogate Predictions with Uncertainties for Materials Science
Quantifying Uncertainty in Atomistic Exploration
Solving Stochastic Inverse Problems for Property–structure Linkages Using Data-consistent Inversion and Machine Learning
Thermodynamic Modeling with Uncertainty Quantification and its Implications for Intermetallic Catalysts Design: Application to Pd-Zn-Based Gamma-Brass Phase
Uncertainty Quantification of a High-throughput Local Plasticity Test: Profilometry-based Indentation Plastometry of Al 7075 T6 Alloy
Uncertainty Quantification of Constitutive Models in Crystal Plasticity Finite Element Method
Using Scalable Multi-Objective Bayesian Optimization to Develop Aluminum Scandium Nitride Molecular Dynamics Force Fields

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