arXiv:2603.21909math.NAcs.LG2026-03被引 1

提出精确满足各类边界条件的新方法,解决复杂曲边域物理信息机器学习难题。

A Novel Method for Enforcing Exactly Dirichlet, Neumann and Robin Conditions on Curved Domain Boundaries for Physics Informed Machine Learning

  • 基于TFC与变分插值的映射方法,精确处理任意四边形域边界条件。
  • 数值误差达机器精度,边界条件完全满足,无残差。
  • 适合需要高精度边界约束的科学计算与工程仿真场景。

本文提出一种系统性方法,可在具有任意曲边的通用四边形域上精确施加狄利克雷、诺伊曼和罗宾型边界条件。该方法基于一般四边形域与标准域间的精确映射,结合理论函数连接(TFC)约束表达式与变分插值技术。当存在诺伊曼或罗宾边界时,尤其在两个此类边界于顶点相交的情形下,必须在交点处精确施加诱导相容性约束,以确保边界条件在连接边界上被完全满足。我们详细分析并构建了两类情形下的处理方案:(i) 诺伊曼(或罗宾)边界仅与狄利克雷边界相交;(ii) 两个诺伊曼(或罗宾)边界相互交叉。文中描述了四步流程,系统推导出在一般四边形域上精确满足狄利克雷、诺伊曼或罗宾条件的函数通解形式。所提方法已与近期开发的极限学习机(ELM)技术集成应用于科学机器学习。大量数值实验涵盖多种二维复杂几何域上的线性/非线性、静态/动态问题。结果表明,该方法在曲边域边界上实现了边界条件的精确满足,数值误差达到机器精度水平。

原文摘要 · Abstract (English)

We present a systematic method for exactly enforcing Dirichlet, Neumann, and Robin type conditions on general quadrilateral domains with arbitrary curved boundaries. Our method is built upon exact mappings between general quadrilateral domains and the standard domain, and employs a combination of TFC (theory of functional connections) constrained expressions and transfinite interpolations. When Neumann or Robin boundaries are present, especially when two Neumann (or Robin) boundaries meet at a vertex, it is critical to enforce exactly the induced compatibility constraints at the intersection, in order to enforce exactly the imposed conditions on the joining boundaries. We analyze in detail and present constructions for handling the imposed boundary conditions and the induced compatibility constraints for two types of situations: (i) when Neumann (or Robin) boundary only intersects with Dirichlet boundaries, and (ii) when two Neumann (or Robin) boundaries intersect with each other. We describe a four-step procedure to systematically formulate the general form of functions that exactly satisfy the imposed Dirichlet, Neumann, or Robin conditions on general quadrilateral domains. The method developed herein has been implemented together with the extreme learning machine (ELM) technique we have developed recently for scientific machine learning. Ample numerical experiments are presented with several linear/nonlinear stationary/dynamic problems on a variety of two-dimensional domains with complex boundary geometries. Simulation results demonstrate that the proposed method has enforced the Dirichlet, Neumann, and Robin conditions on curved domain boundaries exactly, with the numerical boundary-condition errors at the machine accuracy.

物理信息边界条件TFC机器学习

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