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Computation of empirical eigenfunctions of parabolic PDEs with time-varying domain

机译:具有时变域的抛物线型偏微分方程的经验特征函数的计算

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In this work, we explore a methodology to compute the empirical eigenfunctions for the order-reduction of nonlinear parabolic partial differential equations (PDEs) system with time-varying domain. The idea behind this method is to obtain the mapping functional, which relates the time-evolution scalar physical property solution ensemble of the nonlinear parabolic PDE with the time-varying domain to a fixed reference domain, while preserving space invariant properties of the raw solution ensemble. Subsequently, the Karhunen-Lo''eve decomposition is applied to the solution ensemble with fixed spatial domain resulting in a set of optimal eigenfunctions that capture the most energy of data. Further, the low dimensional set of empirical eigenfunctions is mapped (“pushed-back”) on the time-varying domain by an appropriate mapping resulting in the basis for the construction of the reduced-order model of the parabolic PDEs with time-varying domain. Finally, this methodology is applied in the representative cases of calculation of empirical eigenfunctions in the case of one and two dimensional model of nonlinear reaction-diffusion parabolic PDE systems with analytically defined domain evolutions. In particular, the design of both mappings which relate the raw data and function spaces transformations from the time-varying to time-invariant domain are designed to preserve dynamic features of the scalar physical property and we provide comparisons among reduced and high order fidelity models.
机译:在这项工作中,我们探索了一种计算具有时变域的非线性抛物型偏微分方程(PDEs)系统降阶经验特征函数的方法。该方法背后的思想是获得映射函数,该函数将非线性抛物型PDE的时变标量物理性质解集合与时变域关联到固定参考域,同时保留原始解集合的空间不变性。随后,将Karhunen-Lo''eve分解应用于具有固定空间域的解决方案集合,从而生成一组捕获最多数据能量的最佳本征函数。此外,经验特征函数的低维集合通过适当的映射在时变域上映射(“推回”),从而为具有时变域的抛物线型PDE的降阶模型的构建奠定了基础。 。最后,在具有解析定义的域演化的非线性反应扩散抛物线PDE系统的一维和二维模型的情况下,该方法适用于经验特征函数计算的代表性情况。尤其是,将原始数据和从时变域到时不变域的函数空间转换相关联的两个映射的设计都旨在保留标量物理属性的动态特征,并且我们提供了降阶和高阶保真度模型之间的比较。

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