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Concurrent Implementation of the Optimal Incremental Approximation Method for the Adaptive and Meshless Solution of Differential Equations

机译:自适应增量式微分方程最优增量逼近方法的并行实现

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The optimal incremental function approximation method is implemented for the adaptive and meshless solution of differential equations. The basis functions and associated coefficients of a series expansion representing the solution are selected optimally at each step of the algorithm according to appropriate error minimization criteria. Thus, the solution is built incrementally. In this manner, the computational technique is adaptive in nature, although a grid is neither built nor adapted in the traditional sense using a posteriori error estimates. Since the basis functions are associated with the nodes only, the method can be viewed as a meshless method. Variational principles are utilized for the definition of the objective function to be extremized in the associated optimization problems. Complicated data structures, expensive remeshing algorithms, and systems solvers are avoided. Computational efficiency is increased by using low-order local basis functions and the parallel direct search (PDS) optimization algorithm. Numerical results are reported for both a linear and a nonlinear problem associated with fluid dynamics. Challenges and opportunities regarding the use of this method are discussed.
机译:针对微分方程的自适应无网格解实现了最佳增量函数逼近方法。代表解决方案的级数展开式的基函数和相关系数是根据适当的误差最小化准则在算法的每个步骤中最佳选择的。因此,该解决方案是逐步构建的。以这种方式,计算技术本质上是自适应的,尽管使用后验误差估计既没有建立也没有采用传统意义上的网格。由于基本函数仅与节点相关联,因此该方法可以视为无网格方法。利用变分原理来定义要在相关的优化问题中最大化的目标函数。避免了复杂的数据结构,昂贵的刷新算法和系统求解器。通过使用低阶局部基函数和并行直接搜索(PDS)优化算法,可以提高计算效率。报告了与流体动力学相关的线性和非线性问题的数值结果。讨论了有关使用此方法的挑战和机遇。

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