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English(EN) Extended pseudo-spectral physics-informed neural networks for phase-field models

新的ESPINN框架有助于相场模型识别

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了一个扩展伪谱物理信息神经网络（ESPINN）框架，用于从观测数据中识别相场模型。该方法可以同时恢复体化学势和未知的梯度系数，这对于理解相分离中的图案形成至关重要。使用Cahn-Hilliard方程进行的数值实验表明，即使数据有限，ESPINN也能准确地重构这些参数，并且在存在噪声的情况下性能会平稳下降，在快照更多的情况下性能会提高。 AI

影响 该框架提供了一种数据高效的学习复杂物理特性的方法，有可能加速材料科学和其他领域的科学发现。

排序理由 该集群描述了一篇详细介绍用于科学建模的新型神经网络框架的新研究论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →

新的ESPINN框架有助于相场模型识别

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该集群描述了一篇详细介绍用于科学建模的新型神经网络框架的新研究论文。
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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Callum Marsh, Radek Erban, Andreas Munch ·

    用于相场模型的扩展伪谱物理信息神经网络

    arXiv:2606.24660v1 Announce Type: cross Abstract: Phase-field models play a central role in the continuum description of phase separation, in which the bulk free-energy density and the interfacial thickness parameter determine pattern formation and microstructural evolution. In p…

  2. arXiv cs.LG TIER_1 English(EN) · Andreas Munch ·

    用于相场模型的扩展伪谱物理信息神经网络

    Phase-field models play a central role in the continuum description of phase separation, in which the bulk free-energy density and the interfacial thickness parameter determine pattern formation and microstructural evolution. In practice, these constitutive quantities are rarely …

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    用于相场模型的扩展伪谱物理信息神经网络

    Phase-field models play a central role in the continuum description of phase separation, in which the bulk free-energy density and the interfacial thickness parameter determine pattern formation and microstructural evolution. In practice, these constitutive quantities are rarely …

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