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Application-driven model reduction for the simulation of therapeutic infusion processes in multi-component brain tissue

机译:应用驱动的模型简化,用于模拟多组分脑组织中的治疗性输注过程

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The present article concerns the problem-specific application of suitable model-reduction techniques to obtain an efficient numerical simulation of multi-component brain tissue. For this purpose, a compact summary of the underlying theoretical multi-component brain-tissue model is initially introduced in the framework of the Theory of Porous Media (TPM). Typically, the straight-forward monolithic solution of the arising coupled system of equations yields immense numerical costs. Therefore, the primary aim of this work is to apply the method of proper orthogonal decomposition (POD) for a simplified model and the POD in combination with the discrete-empirical-interpolation method (DEIM) for a general nonlinear model in order to reduce the required computation time significantly. Several numerical simulations are realised and discussed in terms of efficiency, accuracy and parameter variations. In conclusion, the article presents necessary adaptations of the POD(-DEIM) allowing for their application to (nonlinear) strongly coupled and multi-component models. (C) 2017 Elsevier B.V. All rights reserved.
机译:本文涉及适当的模型简化技术在特定问题上的应用,以获得多组分脑组织的有效数值模拟。为此,最初在多孔介质理论(TPM)的框架中引入了基础理论多成分脑组织模型的紧凑摘要。通常,出现的耦合方程组的直接整体解决方案会产生巨大的数值成本。因此,这项工作的主要目的是将简化正交模型的适当正交分解(POD)方法和普通非线性模型的离散经验插值方法(DEIM)结合使用,以减少误差。显着地需要计算时间。在效率,准确性和参数变化方面实现并讨论了几种数值模拟。总之,本文介绍了POD(-DEIM)的必要修改,以便将其应用于(非线性)强耦合和多分量模型。 (C)2017 Elsevier B.V.保留所有权利。

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