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

• URL: http://arxiv.org/abs/2603.00093v1
• Date: Tue, 17 Feb 2026 10:43:24 GMT
• Title: Adaptive Uncertainty-Guided Surrogates for Efficient phase field Modeling of Dendritic Solidification
• Abstract summary: This work presents a surrogate model for dendritic solidification that employs uncertainty-driven adaptive sampling with micro XG and CNNs.<n>The framework systematically investigates how temporal selection, adaptive sampling, and the choice between domain-informed data-driven surrogates affect Xtemporal- model performance.
• Score: 0.24629531282150874
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 $CO_2$ emissions, providing a comprehensive assessment of model performance as well as their related environmental impact.

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