论文标题
贝叶斯的方法从无音数据的障碍物发出声音越来越多的时间散射散射
Bayesian Approach to Inverse Time-harmonic Acoustic Scattering from Sound-soft Obstacles with Phaseless Data
论文作者
论文摘要
本文涉及从点源波产生的无量音远场数据来推断声音柔软障碍物的位置和形状的贝叶斯方法。为了提高收敛速率，我们使用Gibbs采样和具有随机建议方差的预处理曲柄 - 尼科尔森（PCN）算法来实施马尔可夫链蒙特卡洛（MCMC）方法。由于未知障碍物在高维度中被参数化，因此通常会导致繁重的计算成本。为了克服这一挑战，我们检查了由广义多项式混乱（GPC）方法构建的替代模型，以降低计算成本。提供了数值示例来说明所提出的方法的有效性。
This paper 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 point source waves. To improve the convergence rate, we use the Gibbs sampling and preconditioned Crank-Nicolson (pCN) algorithm with random proposal variance to implement the Markov chain Monte Carlo (MCMC) method. This usually leads to heavy computational cost, since the unknown obstacle is parameterized in high dimensions. To overcome this challenge, we examine a surrogate model constructed by the generalized polynomial chaos (gPC) method to reduce the computational cost. Numerical examples are provided to illustrate the effectiveness of the proposed method.
