Collaborative Research: Scalable and accurate direct solvers for integral equations on surfaces

协作研究：可扩展且精确的曲面积分方程直接求解器

• 批准号: 1320652
• 负责人: Per-Gunnar Martinsson
• 金额: $ 21.92万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320652&HistoricalAwards=false
• 关键词: Collaborative; Research; Scalable; accurate; direct

The goal of the proposed research is to develop faster and more accurate algorithms for computing approximate solutions to a broad class of equations that model physical phenomena such as heat transport, deformation of elastic bodies, scattering of electromagnetic waves, and many others. The task of solving such equations is frequently the most time consuming part of computational simulations, and is the part that determines which problems can be modeled computationally, and which cannot. Dealing with complicated shapes (e.g. scattering from complex geometry or flow through channels of complicated shape) adds difficulty to the computational task.Technically speaking, most existing large-scale numerical algorithms for solving partial differential and integral equations on complex geometries are based on so called "iterative methods" which construct a sequence of approximate solutions that gradually approach the exact solution. The proposed research seeks to develop "direct methods" for solving equations. A "direct method" computes the unknown data from the given data in one shot. When available, direct methods are often preferred to iterative ones since they are more robust, and can be used in a "black-box" way. As a result these are more suitable for incorporation in general purpose software, and in many cases work for important problems that cannot be solved with existing iterative methods. The reason that they are today typically not used is that existing direct methods for many problems are often prohibitively expensive. However, recent results by the PIs and other researchers have proven that it is possible to construct direct methods that are competitive in terms of speed with the very fastest existing iterative solvers. The new algorithms will be applied to the simulation of fluid flows and biomolecular simulations, and their performance will be demonstrated by the execution of simulations on complex geometries.

拟议研究的目标是开发更快，更准确的算法，用于计算广泛的一类方程的近似解，这些方程模拟物理现象，如热传输，弹性体变形，电磁波散射等。求解此类方程的任务通常是计算模拟中最耗时的部分，并且是确定哪些问题可以通过计算建模，哪些不能的部分。处理复杂形状（例如复杂几何形状的散射或复杂形状通道中的流动）增加了计算任务的难度。从技术上讲，大多数现有的求解复杂几何形状上的偏微分和积分方程的大规模数值算法都是基于所谓的“迭代方法”，即构造一系列逐渐接近精确解的近似解。拟议的研究旨在开发求解方程的“直接方法”。“直接法”从给定的数据中一次性计算出未知数据。 在可用的情况下，直接方法通常比迭代方法更受欢迎，因为它们更鲁棒，并且可以以“黑箱”的方式使用。 因此，这些更适合于纳入通用软件，并在许多情况下，工作的重要问题，不能解决现有的迭代方法。它们今天通常不被使用的原因是，用于许多问题的现有直接方法通常过于昂贵。然而，PI和其他研究人员最近的结果已经证明，有可能构建直接方法，在速度方面与现有最快的迭代求解器竞争。新算法将被应用于流体流动和生物分子模拟的模拟，其性能将通过复杂几何形状的模拟来证明。