论文标题
数据驱动的贝叶斯反演的前向离散化
Data-Driven Forward Discretizations for Bayesian Inversion
论文作者
论文摘要
本文提出了一个学习贝叶斯反问题中昂贵远期模型的离散化的框架。主要思想是将管理离散化的参数合并为在贝叶斯机械中估计的未知数的一部分。我们从数值上表明，在机械工程，信号处理和地球科学中引起的各种反问题中，观察结果包含有用的信息来指导离散化的选择。
This paper suggests a framework for the learning of discretizations of expensive forward models in Bayesian inverse problems. The main idea is to incorporate the parameters governing the discretization as part of the unknown to be estimated within the Bayesian machinery. We numerically show that in a variety of inverse problems arising in mechanical engineering, signal processing and the geosciences, the observations contain useful information to guide the choice of discretization.
