About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Gaussian Process as a Flexible Machine Learning Toolbox with Uncertainty Quantification for Solving Inverse Problems in Process-Structure-Property Relationship |
Author(s) |
Anh Tran, Tim Wildey |
On-Site Speaker (Planned) |
Anh Tran |
Abstract Scope |
Process-structure property is the celebrated hallmark of materials science. Numerous ICME models have been developed to enable accurate numerical predictions, where uncertainty plays a key role in verification and validation. While forward models are widely established, inverse problems are less so due to the expensive computational cost of the forward models. The Gaussian process is a powerful and flexible machine learning toolbox that offers a broad possibility for machine learning in materials science applications, including data-efficient active machine learning approaches such as Bayesian optimization. In this work, we utilize Gaussian process and Bayesian optimization to approximate ICME models and solve deterministic/stochastic inverse problems in process-structure and structure-property relationships. We demonstrate the usefulness of the Gaussian process with two ICME examples: kinetic Monte Carlo and crystal plasticity finite element models. |
Proceedings Inclusion? |
Undecided |