论文标题
流体动态工程问题的数据驱动的POD框架内的高斯流程方法
Gaussian process approach within a data-driven POD framework for fluid dynamics engineering problems
论文作者
论文摘要
这项工作描述了采用适当的正交分解（POD）和高斯过程回归（GPR）的参数偏微分方程（PDE）的复杂性的数据驱动方法的实现。最初，这种方法应用于文献案例，Stokes问题的仿真以及以下是现实世界中的工业问题，内部的形状优化管道用于海军工程问题。
This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR). This approach is applied initially to a literature case, the simulation of the stokes problems, and in the following to a real-world industrial problem, inside a shape optimization pipeline for a naval engineering problem.
