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English(EN) A mesh-free multiresolution deep energy method with phase-field modeling of brittle fracture

新型无网格神经网络高精度模拟脆性断裂

研究人员开发了一种新颖的无网格方法,使用单个神经网络来模拟脆性断裂,无需显式裂纹跟踪。该方法采用基于B样条网格的多分辨率特征编码和分层蒙特卡洛积分来表示位移和相场。该方法在各种断裂问题中表现出强大的性能,在载荷-位移曲线精度方面可与有限元方法相媲美,并在裂纹分类基准测试中优于深度Ritz基线。 AI

影响 引入了一种新颖的神经网络方法用于复杂的物理模拟,有望加速材料科学和工程领域的研究。

排序理由 学术论文,详细介绍了一种新的计算方法。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新型无网格神经网络高精度模拟脆性断裂

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Han Zhang, Mehrisadat Makki Alamdari, Babak Shahbodagh, Mohammad Vahab, Cosmin Anitescu, Timon Rabczuk, Elena Atroshchenko ·

    一种无网格多分辨率深度能量方法结合脆性断裂的相场模型

    arXiv:2608.24126v1 Announce Type: new Abstract: Phase-field modeling of brittle fracture removes the need to track cracks explicitly by recasting their evolution as the minimization of an energy functional. In return it requires a discretization dense enough to resolve a localiza…

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

    一种无网格多分辨率深度能量法结合脆性断裂相场模型

    Phase-field modeling of brittle fracture removes the need to track cracks explicitly by recasting their evolution as the minimization of an energy functional. In return it requires a discretization dense enough to resolve a localization band whose width is set by a regularization…