報告題目：Adaptive multi-fidelity surrogate modeling for Bayesian inference in inverse problems
報 告 人：周濤 中國科學院數學與系統科學研究院
報告時間：2020年6月21日上午 10:00-11:00
報告地點：騰訊會議 ID：503 563 582
會議密碼：200621
或添加至會議列表：
https://meeting.tencent.com/s/D8SrYM3jWlOx
校内聯系人：張凱 zhangkaimath@jlu.edu.cn
報告摘要：
The generalized polynomial chaos (gPC) are widely used as surrogate models in Bayesian inference to speed up the Markov chain Monte Carlo simulations. However, the use of gPC-surrogates introduces model errors that may severely distort the estimate of the posterior distribution. In this talk, we present an adaptive procedure to construct an adaptive gPC-surrogate. The key idea is to refine the surrogate over a sequence of samples adaptively so that the surrogate is much more accurate in the posterior region. We then introduce an adaptive surrogate modeling approach based on deep neural networks to handle problems with high dimensional parameters.
報告人介紹：
Tao Zhou is currently an Associate Professor in Chinese Academy of Sciences. Before joining CAS, he was a postdoc fellow in EPFL in Switzerland during 2011-2012. Dr. Zhou’s research interests include Uncertainty Quantification (UQ), Parallel-in-Time Algorithms, Spectral Methods and Stochastic Optimal Control. He has published more than 50 papers in top international journals such as SIAM Review, SINUM and JCP. He was a recipient of the NSFC Career Award for Excellent Young Scholars (2018) and CSIAM Excellent Young Scholar Prize (2016). Dr. Zhou serves as Associate Editor for many international journals such as SIAM Journal on Scientific Computing (SISC) and Communications in Computational Physics (CiCP). He also serves as the Associate Editor-in-Chief of International Journal for UQ. Since 2018, he has been the Chief Scientist of Science Challenge Project on UQ supported by State Administration of Science, Technology and Industry for National Defense.
