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Meeting 2024 TMS Annual Meeting & Exhibition
Symposium AI/Data Informatics: Computational Model Development, Verification, Validation, and Uncertainty Quantification
Presentation Title Stochastic Inverse Microstructure Design
Author(s) Adam Generale, Andreas Robertson, Conlain Kelly, Surya Kalidindi
On-Site Speaker (Planned) Adam Generale
Abstract Scope A central goal of Materials Informatics involves inverting the relationship between microstructure and property. This problem is inherently ill-posed due to both the non-unique mapping between microstructure and property, and the stochastic nature of microstructure itself. We propose a framework for solving such stochastic microstructure inverse problems through Bayesian inversion. Microstructure descriptors are formed through a chained transformation - composed of a 2-point statistics derived space and a learned latent space, combining the strengths of both statistical approaches and learning. This dichotomy facilitates a solution space comprised of high dimensional microstructures with complex local states and spatial patterns. Inference in the resulting latent space is performed using a flow-based generative model while accounting for forward model uncertainty. Application of the framework is then demonstrated in the design of woven composites with 3 local material states, conditioned upon a multi-objective target of orthotropic thermal conductivity.
Proceedings Inclusion? Planned:
Keywords ICME, Machine Learning, High-Temperature Materials

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