风速体育(集团)体育科技有限公司官网-风速体育

Deep learning approach for Bayesian inverse Problems

发布时间：2022-06-13 作者：浏览次数：

Speaker:	闫亮	DateTime:	2022年-6月-17日（周五）上午9：30-10：30
Brief Introduction to Speaker: 闫亮，东南大学副教授		Place:	腾讯会议 ID 719 103 888
Abstract:Obtaining samples from the posterior distribution of Bayesian inverse problems(BIPs) is a long-standing challenging, especially when the forward operator is modeled by partial differential equation (PDE). In this talk, we will show you how to leverage the deep learning’s capabilities to tackle this challenge. Several fast and efficient deep neural network (DNN)-based approaches for accelerating simulations in sample generation will be described. A novel framework based on invertible neural networks using normalizing flow is also demonstrated.