We describe a new, adaptive solver for the two-dimensional Poisson equation in complicated geometries. Using classical potential theory, we represent the solution as the sum of a volume potential and a double layer potential. Rather than evaluating the volume potential over the given domain, we first extend the source data to a geometrically simpler region with high order accuracy. This allows us to accelerate the evaluation of the volume potential using an efficient, geometry-unaware fast multipole-based algorithm. To impose the desired boundary condition, it remains only to solve the Laplace equation with suitably modified boundary data. This is accomplished with existing fast and accurate boundary integral methods. The novelty of our solver is the scheme used for creating the source extension, assuming it is provided on an adaptive quad-tree. For leaf boxes intersected by the boundary, we construct a universal "stencil" and require that the data be provided at the subset of those points that lie within the domain interior. This universality permits us to precompute and store an interpolation matrix which is used to extrapolate the source data to an extended set of leaf nodes with full tensor-product grids on each. We demonstrate the method's speed, robustness and high-order convergence with several examples, including domains with piecewise smooth boundaries.
翻译:我们用复杂的地貌来描述二维 Poisson 方程式的一个新的适应性求解器。 我们使用古典的潜在理论, 将解决方案作为数量潜力和双层潜力的总和来代表。 我们首先将源数据推广到一个具有高顺序精确度的几何简单区域, 从而使我们能够使用高效的、 几何- 软件快速多极算法加速评估体积潜力。 要强制实施理想的边界条件, 它只能用适当修改的边界数据来解决 Laplace 方程式。 这是利用现有快速准确的边界整体方法完成的。 我们的解答器的新颖性是用来创建源扩展的图案, 假设它是在适应性四叶树上提供的。 对于受边界交错的叶片框, 我们建造了一个通用的“ 线状”, 并要求在位于域内的各个点的子组中提供数据。 这个普遍性允许我们预先校准并存储一个内置源数据矩阵, 用来将源数据外加成一个扩展的节叶片断集集集集集, 包括全色、 平流度高速度, 以若干 度的平流式的模型展示。