Extrapolating Phase-Field Simulations in Space and Time with Purely Convolutional Architectures
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914055989493760 |
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| author | Bonneville, Christophe Bieberdorf, Nathan Robbe, Pieterjan Asta, Mark Najm, Habib N. Capolungo, Laurent Safta, Cosmin |
| author_facet | Bonneville, Christophe Bieberdorf, Nathan Robbe, Pieterjan Asta, Mark Najm, Habib N. Capolungo, Laurent Safta, Cosmin |
| contents | Phase-field models of liquid metal dealloying (LMD) can resolve rich microstructural dynamics but become intractable for large domains or long time horizons. We present a conditionally parameterized, fully convolutional U-Net surrogate that generalizes far beyond its training window in both space and time. The design integrates convolutional self-attention and physics-aware padding, while parameter conditioning enables variable time-step skipping and adaptation to diverse alloy systems. Although trained only on short, small-scale simulations, the surrogate exploits the translational invariance of convolutions to extend predictions to much longer horizons than traditional solvers. It accurately reproduces key LMD physics, with relative errors typically under 5% within the training regime and below 10% when extrapolating to larger domains and later times. The method accelerates computations by up to 16,000 times, cutting weeks of simulation down to seconds, and marks an early step toward scalable, high-fidelity extrapolation of LMD phase-field models. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_20770 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extrapolating Phase-Field Simulations in Space and Time with Purely Convolutional Architectures Bonneville, Christophe Bieberdorf, Nathan Robbe, Pieterjan Asta, Mark Najm, Habib N. Capolungo, Laurent Safta, Cosmin Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning Numerical Analysis Phase-field models of liquid metal dealloying (LMD) can resolve rich microstructural dynamics but become intractable for large domains or long time horizons. We present a conditionally parameterized, fully convolutional U-Net surrogate that generalizes far beyond its training window in both space and time. The design integrates convolutional self-attention and physics-aware padding, while parameter conditioning enables variable time-step skipping and adaptation to diverse alloy systems. Although trained only on short, small-scale simulations, the surrogate exploits the translational invariance of convolutions to extend predictions to much longer horizons than traditional solvers. It accurately reproduces key LMD physics, with relative errors typically under 5% within the training regime and below 10% when extrapolating to larger domains and later times. The method accelerates computations by up to 16,000 times, cutting weeks of simulation down to seconds, and marks an early step toward scalable, high-fidelity extrapolation of LMD phase-field models. |
| title | Extrapolating Phase-Field Simulations in Space and Time with Purely Convolutional Architectures |
| topic | Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2509.20770 |