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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Modeling Stochastic Dynamics by Transforming Conditional Densities with Amortized Conditional Optimal Transport

Author(s)

Adam Generale, Andreas Robertson, Surya Kalidindi

On-Site Speaker (Planned)

Adam Generale

Abstract Scope

Forecasting conditional stochastic nonlinear dynamical systems is a central challenge inherent in modeling the evolution pathways of materials' microstructures during processing. Data acquisition in these settings is particularly difficult, often necessitating destructive testing to assess the material’s internal state -- precluding the observation of complete temporal trajectories. Flow-based generative models have demonstrated remarkable capability in leveraging such data to predict the evolution of distributions over time. However, existing methodologies fall short in rigorously capturing the influence of conditioning variables on these dynamics. To overcome these limitations, we introduce a framework that integrates simultaneously conditioned flows with a conditional Wasserstein distance and a reweighting kernel towards further facilitating conditional optimal transport. We evaluate our approach on a series of increasingly complex tasks, encompassing discrete and continuous conditional mapping benchmarks, image-to-image domain transfer problems, and the modeling of temporal microstructural evolution in materials under processing.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, ICME,

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