时 间：2024年6月7日
地 点：普陀校区理科大楼A1716
报告人：王文佳 香港科技大学（广州） 助理教授
主持人：王亚平 华东师范大学教授
摘 要：
Surrogate modeling based on Gaussian processes (GPs) is becoming increasingly popular in analysis of complex problems in science and engineering. However, despite the many studies on GP modeling, few focus on functional inputs. Motivated by an inverse scattering problem in which functional inputs representing the support and material properties of the scatterer are involved in the partial differential equations, we propose a new class of kernel functions for functional inputs of GPs. Based on the proposed GP models, we derive the asymptotic convergence properties of the resulting mean squared prediction errors, and demonstrate the finite-sample performance using numerical examples. In the application to inverse scattering, we construct a surrogate model with functional inputs, which is crucial to recovering the reflective index of an inhomogeneous isotropic scattering region of interest for a given far-field pattern
报告人简介：
王文佳是香港科技大学（广州）信息枢纽数据科学与分析学域的助理教授；2018年8月获得佐治亚理工学院工业工程系博士学位。王文佳的研究方向包括不确定性量化、随机仿真、机器学习、非参数统计和计算机实验。目前已在统计学、机器学习、管理学顶级期刊、会议Journal of the American Statistical Association，Journal of Machine Learning Research，Management Science，Technometrics，NeurIPS，ICLR，ICML等发表多篇文章。