摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-关于机器学习的详细数据已经呈现。根据新闻报道来自英国格拉斯哥的Ne wsRx记者的研究表明，“我们引入了一个框架，用于精确执行线性边界约束高斯过程(BCGP)先验设计并将其应用于线性(初始)边值问题的机器学习偏微分方程(PDEs)。本文结合已有的工作,阐述了边界的设计一般用于偏微分方程模式的各类边界条件的约束均值函数和Ke rnel函数Lling，即Dirichlet，Neumann，Robin和混合条件。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Data detailed on Machine Learning have been presented. According to news reportingfrom Glasgow, United Kingdom, by Ne wsRx journalists, research stated, “We introduce a framework fordesigning bound ary constrained Gaussian process (BCGP) priors for exact enforcement of linear b oundaryconditions, and apply it to the machine learning of (initial) boundary v alue problems involving linearpartial differential equations (PDEs). In contras t to existing work, we illustrate how to design boundaryconstrained mean and ke rnel functions for all classes of boundary conditions typically used in PDE modelling, namely Dirichlet, Neumann, Robin and mixed conditions.”
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