物理信息神经网络中的频谱偏置和内核任务对齐

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Inbar Seroussi, Asaf Miron, Zohar Ringel
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引用次数: 0

摘要

物理信息神经网络(PINN)是解决微分方程的一种前景广阔的新兴方法。与许多其他深度学习方法一样,PINN 设计和训练协议的选择也需要精雕细琢。在此,我们提出了一个全面的理论框架,以揭示这一重要问题。利用无限过参数化神经网络和高斯过程回归之间的等价关系,我们推导出了一个在大数据集限制下支配 PINN 预测的积分微分方程--神经信息方程。该方程通过一个反映架构选择的内核项来增强原始方程。通过对原始微分方程中的源项进行频谱分解,它可以量化网络引起的隐含偏差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spectral-bias and kernel-task alignment in physically informed neural networks
Physically informed neural networks (PINNs) are a promising emerging method for solving differential equations. As in many other deep learning approaches, the choice of PINN design and training protocol requires careful craftsmanship. Here, we suggest a comprehensive theoretical framework that sheds light on this important problem. Leveraging an equivalence between infinitely over-parameterized neural networks and Gaussian process regression, we derive an integro-differential equation that governs PINN prediction in the large data-set limit—the neurally-informed equation. This equation augments the original one by a kernel term reflecting architecture choices. It allows quantifying implicit bias induced by the network via a spectral decomposition of the source term in the original differential equation.
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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