题    目：Bayesian Approach to Inverse Time-harmonic Acoustic Obstacle Scattering with Phaseless Data

主讲人：杨志鹏 副教授

单    位：兰州大学

时    间：2025年1月16日 14:00

腾讯ID：983-764-305

密    码：250116

摘    要：This talk concerns the Bayesian approach to inverse acoustic scattering problems of inferring the position and shape of a sound-soft obstacle from phaseless far-field data generated by two-dimensional point source waves. Given the total number of obstacle parameters, the Markov chain Monte Carlo (MCMC) method is employed to reconstruct the boundary of the obstacle in a high-dimensional space, which usually leads to slow convergence and prohibitively high computational cost. We use the Gibbs sampling and preconditioned Crank-Nicolson (pCN) algorithm with random proposal variance to improve the convergence rate, and design an effective strategy for the surrogate model constructed by the generalized polynomial chaos (gPC) method to reduce the computational cost of MCMC.

简    介：杨志鹏，兰州大学数学与统计学院副教授。2022年-2024年在南方科技大学数学系从事博士后研究工作，2024年至今为兰州大学数学与统计学院副教授。主要研究方向为不确定性量化、反问题的数值方法等。主持中国博士后科学基金面上项目一项，国家自然科学基金青年项目一项。目前已在IP、CMAME、JSC、SIIMS、AMO、IPI、CAMWA等杂志发表多篇文章。