Reduced Basis Methods (RBMs) are frequently proposed to approximate parametric problem solutions. They can be used to calculate solutions for a large number of parameter values (e.g. for parameter fitting) as well as to approximate a solution for a new parameter value (e.g. real time approximation with a very high accuracy). They intend to reduce the computational costs of High Fidelity (HF) codes. We will focus on the Non-Intrusive Reduced Basis (NIRB) two-grid method. Its main advantage is that it uses the HF code exclusively as a "black-box," as opposed to other so-called intrusive methods that require code modification. This is very convenient when the HF code is a commercial one that has been purchased, as is frequently the case in the industry. The effectiveness of this method relies on its decomposition into two stages, one offline (classical in most RBMs as presented above) and one online. The offline part is time-consuming but it is only performed once. On the contrary, the specificity of this NIRB approach is that, during the online part, it solves the parametric problem on a coarse mesh only and then improves its precision. As a result, it is significantly less expensive than a HF evaluation. This method has been originally developed for elliptic equations with finite elements and has since been extended to finite volume. In this paper, we extend the NIRB two-grid method to parabolic equations. We recover optimal estimates in $L^{\infty}(0,T;H^1(\Omega))$ using as a model problem, the heat equation. Then, we present numerical results on the heat equation and on the Brusselator problem.
翻译:常提议降低基础方法(RBM) 以近似于参数问题解决方案。 它们可以用来计算大量参数值的解决方案( 例如, 参数安装) 和新参数值的解决方案( 例如, 实时近似, 精度很高 ) 。 它们打算降低高Fidility( 高频) 代码的计算成本 。 我们将侧重于“ 非侵入性降低基( NIRB) 双网格 ” 方法。 它的主要优势在于它只使用高频代码作为“ 黑盒 ”, 而不是其他需要修改代码的所谓侵入性方法。 当高频代码是已经购买的商业参数值时( 例如, 实时近距离近距离近距离近距离近, 精度 ) 。 我们的离线部分很费, 但仅一次运行一次。 与此相反, NIBBT 方法的特殊性是, 在网络部分, 将高频 平方程式1 的计算结果, 也就是在原始的精确度中, 我们的精确度1, 将这一方法推延到 。