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English(EN) From Non-Convex Self-Concordant Regularization to Scalable Quasi-Newton Training of PINNs

新的SCORE方法增强了物理信息神经网络的训练

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的、受自协调启发的伪牛顿方法SCORE，旨在改进物理信息神经网络（PINNs）的训练。该方法通过使用伪牛顿递减来联合确定割线几何的候选步长和自适应偏移量，从而解决了PINN目标中曲率不确定和尺度不良的问题。在包括粘性Burgers方程在内的多个偏微分方程上的实验表明，与现有的BFGS和自缩放Broyden基线相比，SCORE实现了更低的最终误差。 AI

影响 这种新方法有望为用神经网络建模的复杂科学问题带来更准确、更有效的解决方案。

排序理由 该集群包含一篇详细介绍一种新神经网络训练方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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新的SCORE方法增强了物理信息神经网络的训练

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chenhao Si, Kang An, Shiqian Ma, Ming Yan ·

    从非凸自协调正则化到可扩展的伪牛顿物理信息神经网络训练

    arXiv:2608.04206v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) often require high-accuracy quasi-Newton refinement to obtain reliable partial differential equation solutions, but their residual objectives can exhibit indefinite, nearly singular, and poor…

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