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Meeting MS&T22: Materials Science & Technology
Symposium Uncertainty Quantification in Data-Driven Materials and Process Design
Presentation Title Solving Stochastic Inverse Problems for Property–structure Linkages Using Data-consistent Inversion and Machine Learning
Author(s) Tim Wildey, Anh Tran
On-Site Speaker (Planned) Tim Wildey
Abstract Scope Determining process–structure–property linkages is one of the key objectives in material science, and uncertainty quantification plays a critical role in understanding both process–structure and structure–property linkages. In this presentation, we demonstrate how to learn a distribution of microstructure parameters that are consistent in the sense that the forward propagation of this distribution through a computational model matches a target distribution on materials properties. This stochastic inversion formulation infers a distribution of acceptable/consistent microstructures, as opposed to a deterministic solution, which expands the range of feasible designs. To solve this stochastic inverse problem, we employ a recently developed uncertainty quantification framework based on push-forward probability measures to define a unique and numerically stable solution. To reduce the computational burden in solving both stochastic forward and stochastic inverse problems, we combine this approach with a machine learning surrogate models and demonstrate the proposed methodology on two representative case studies in structure–property linkages.

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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