# Adaptive Uncertainty-Guided Surrogates for Efficient phase field Modeling of Dendritic Solidification

arXiv: https://arxiv.org/abs/2603.00093 · HTML: https://mlsanity.com/paper/2603.00093/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2026.

## Abstract (arXiv)

The high computational cost of phase field simulations remains a major limitation for predicting dendritic solidification in metals, particularly in additive manufacturing, where microstructural control is critical. This work presents a surrogate model for dendritic solidification that employs uncertainty-driven adaptive sampling with XGBoost and CNNs, including a self-supervised strategy, to efficiently approximate the spatio-temporal evolution while reducing costly phase field simulations. The proposed adaptive strategy leverages model uncertainty, approximated via Monte Carlo dropout for CNNs and bagging for XGBoost, to identify high-uncertainty regions where new samples are generated locally within hyperspheres, progressively refining the spatio-temporal design space and achieving accurate predictions with significantly fewer phase field simulations than an Optimal Latin Hypercube Sampling optimized via discrete Particle Swarm Optimization (OLHS-PSO). The framework systematically investigates how temporal instance selection, adaptive sampling, and the choice between domain-informed and data-driven surrogates affect spatio-temporal model performance. Evaluation considers not only computational cost but also the number of expensive phase field simulations, surrogate accuracy, and associated CO2 emissions, providing a comprehensive assessment of model performance as well as their related environmental impact.