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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

Accelerating Microstructural Solidification Simulations with Deep Neural Surrogates in Additive Manufacturing Conditions

Author(s)

Simon Savukoski, Joni Kaipainen, Mikko Tahkola, Nikolas Provatas, Anssi Laukkanen, Tatu Pinomaa

On-Site Speaker (Planned)

Simon Savukoski

Abstract Scope

Phase-field models resolve microstructural solidification in metal additive manufacturing, but their high computational cost remains a bottleneck—especially for high-throughput and 3D simulations. We develop neural surrogates—deterministic (AFNO, UNet) and stochastic (variational autoencoders, diffusion models)—to replace phase-field simulations. Focusing on Al–Cu alloy, the models predict order parameter and concentration evolution over a range of thermal gradients and pulling speeds. We compare several training loss metrics, such as pixel-wise comparison, mass conservation, and phase fraction. Models are trained under varying spatio-temporal downsampling strategies to assess resolution–performance tradeoffs. Predictive accuracy is measured via 2-point correlation, solute conservation, phase fractions and cell/dendrite spacing relative to the ground truth. The time complexity of surrogate inference and the phase-field simulations runtime is compared. Physics-informed approaches and extension to 3D are discussed. These surrogates provide significant acceleration over phase field simulations, offering a scalable path toward prediction of additively manufactured microstructures.

Proceedings Inclusion?

Planned:

Keywords

Additive Manufacturing, Solidification, Machine Learning

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