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An hp certified reduced basis method for parametrized parabolic partial differential equations

机译:hp认证的简化的方法,用于参数化抛物型偏微分方程

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In this article, we introduce an hp certified reduced basis (RB) method for parabolic partial differential equations. We invoke a Proper Orthogonal Decomposition (POD) (in time)/Greedy (in parameter) sampling procedure first in the initial partition of the parameter domain (h-refinement) and subsequently in the construction of RB approximation spaces restricted to each parameter subdomain (p-refinement). We show that proper balance between additional POD modes and additional parameter values in the initial subdivision process guarantees convergence of the approach. We present numerical results for two model problems: linear convection-diffusion and quadrat-ically non-linear Boussinesq natural convection. The new procedure is significantly faster (more costly) in the RB Online (Offline) stage.
机译:在本文中,我们为抛物线偏微分方程引入了hp认证的减基(RB)方法。我们首先在参数域的初始分区(h-精炼)中调用适当的正交分解(POD)(时间)/贪婪(在参数中)采样过程,然后在构造限制于每个参数子域的RB近似空间( p优化)。我们表明,在初始细分过程中,其他POD模式和其他参数值之间的适当平衡可确保方法的收敛。我们给出两个模型问题的数值结果:线性对流扩散和二次非线性Boussinesq自然对流。在RB在线(脱机)阶段,新过程明显更快(成本更高)。

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