Eighth Copper Mountain Conference on Multigrid Methods

首席研究员麦考密克9614997和他的同事们与工业和应用数学学会合作，组织了第八届铜山多网格方法会议。这笔助学金为参加会议的学生提供支持。求解微分方程的数值方法通常首先在方程所在的区域上施加网格。从微分方程式出发，发展出代数方程；它们的解代表了微分方程式的解。近似解的精度通常用网格的细度来衡量。多重网格法是一种求解偏微分方程组的数值方法，它系统地利用不同网格上近似解之间的关系，得到一个精度与最细网格一致但工作量相当小的解。这些方法往往比其他方法效率高得多。在过去的十几年中，研究已经将这些方法扩展到在工程、制造、材料、物理和流体动力学中具有相当重要的实际意义的广泛问题。该项目支持参加第八届铜山多网格方法会议的学生。学生们在会议的定期会议上发表了关于他们的研究的报告。这里的主要目标是鼓励学生参与这个快速发展的领域，并为这些学生提供一个极好的机会来展示他们的新成果，从该领域的专家那里了解更多关于该领域的知识，并成为该学科更不可或缺的一部分。支持学生参与对于改善这一重要领域的基础设施和提高其核心贡献者的素质至关重要。

McCormick 9614997 The principal investigator and his colleagues organize the eighth Copper Mountain conference on multigrid methods, in cooperation with the Society of Industrial and Applied Mathematics. This grant provides support for students to participate in the conference. Numerical methods for solving a differential equation usually begin by imposing a grid on the region where the equation holds. From the differential equation, algebraic equations are then developed; their solution represents the solution of the differential equation. The accuracy of the approximate solution commonly is measured by the fineness of the grid. Multigrid methods are numerical methods for solving partial differential equations that systematically exploit the relationship between approximate solutions on different grids to arrive at a solution whose accuracy is consistent with the finest grid but for considerably less work. The methods are often dramatically more efficient than others. Research in the past dozen years has extended the methods to a broad range of problems of considerable practical import in engineering, manufacturing, materials, physics, and fluid dynamics. This project supports student participants at the eighth Copper Mountain conference on multigrid methods. The students present a talk on their research in the regular sessions of the conference. The primary objective here is to encourage student participation in this rapidly evolving field, and to provide an excellent opportunity for these students to demonstrate their new results, to learn more about the field from its experts, and to become a more integral part of the discipline. Supporting student participation is critical for improving the infrastructure of this important field and for advancing the quality of its core contributors.