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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.00093 |
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| _version_ | 1866910035694583808 |
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| author | Garate-Perez, Eider de Calle-Etxabe, Kerman López Garcia, Oihana Calvo, Borja Gómez-Omella, Meritxell Lambarri, Jon |
| author_facet | Garate-Perez, Eider de Calle-Etxabe, Kerman López Garcia, Oihana Calvo, Borja Gómez-Omella, Meritxell Lambarri, Jon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_00093 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adaptive Uncertainty-Guided Surrogates for Efficient phase field Modeling of Dendritic Solidification Garate-Perez, Eider de Calle-Etxabe, Kerman López Garcia, Oihana Calvo, Borja Gómez-Omella, Meritxell Lambarri, Jon Computational Physics Artificial Intelligence Machine Learning 68 I.2; J.2 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. |
| title | Adaptive Uncertainty-Guided Surrogates for Efficient phase field Modeling of Dendritic Solidification |
| topic | Computational Physics Artificial Intelligence Machine Learning 68 I.2; J.2 |
| url | https://arxiv.org/abs/2603.00093 |