论文标题
Pod-Greedy-galerkin简介还原方法
An introduction to POD-Greedy-Galerkin reduced basis method
论文作者
论文摘要
部分微分方程可用于模拟多个应用领域的许多问题，例如流体力学，热量和传质以及电磁作用。准确的离散方法（例如有限元或有限体积方法，所谓的全订单模型）被广泛用于数值解决这些问题。但是，当涉及许多物理和/或几何参数时，全订单模型所需的计算成本变得过于昂贵，并且对于快速原型越来越流行的实时计算，这是不可接受的。因此，有必要以降低的计算成本以快速和可靠的解决方案的变化，引入减少订单方法（也称为减少的基础方法）能够提供。
Partial differential equations can be used to model many problems in several fields of application including, e.g., fluid mechanics, heat and mass transfer, and electromagnetism. Accurate discretization methods (e.g., finite element or finite volume methods, the so-called full order models) are widely used to numerically solve these problems. However, when many physical and/or geometrical parameters are involved, the computational cost required by full order models becomes prohibitively expensive and this is not acceptable for real-time computations that are becoming more and more popular for rapid prototyping. Therefore, there is the need to introduce reduced order methods (also referred to as reduced basis methods) able to provide, as the input parameters change, fast and reliable solutions at a reduced computational cost.
