首页> 外文期刊>Numerical Heat Transfer, Part B. Fundamentals: An International Journal of Computation and Methodology >REDUCED-BASIS APPROXIMATION AND A POSTERIORI ERROR ESTIMATION FOR MANY-PARAMETER HEAT CONDUCTION PROBLEMS
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REDUCED-BASIS APPROXIMATION AND A POSTERIORI ERROR ESTIMATION FOR MANY-PARAMETER HEAT CONDUCTION PROBLEMS

机译:多参数传热问题的降基逼近和后误差估计

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摘要

Reduced-basis (RB) methods enable repeated and rapid evaluation of parametrized partial differential equation (PDE)-constrained input-output relationships required in the context of parameter estimation, design, optimization, and control. These methods have been successfully applied to problems with few parameters [O(3)]. Here we introduce efficient sampling algorithms that enable the efficient exploration of many parameters. We apply the RB methods to an illustrative heat conduction problem with P = 25 parameters, obtaining accurate and certified results in real time with significant computational savings relative to standard finite-element techniques.
机译:减少基数(RB)方法可以在参数估计,设计,优化和控制的情况下,重复快速评估参数化偏微分方程(PDE)约束的输入输出关系。这些方法已成功应用于参数较少的问题[O(3)]。在这里,我们介绍了有效的采样算法,可以有效地探索许多参数。我们将RB方法应用于P = 25参数的说明性导热问题,与标准的有限元技术相比,可以实时获得准确且经过认证的结果,并且可以节省大量计算量。

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