应学院邀请，东南大学闫亮副教授将在线作学术报告。
报告题目：Adaptive Surrogate Modeling Based on Deep Neural Networks for Bayesian Inverse Problems
报告摘要：Surrogate models are often constructed to speed up the computational procedure of the Bayesian inverse problems(BIPs), as the forward models can be very expensive to evaluate. However, due to the curse of dimensionality and the nonlinear concentration of the posterior, traditional surrogate approaches are still not feasible for large scale problems. This talk will survey our recent works in designing surrogate models using deep learning techniques. Several fast and efficient algorithms based on deep neural networks(DNN) to solve BIPs will be covered, including adaptive multi-fidelity surrogate modeling and local approximations. Numerical examples are presented to confirm that new approaches can obtain accurate posterior information with a limited number of forward simulations.
报告时间：2022年5月20日上午8:30-10:30
报告地点：腾讯会议(会议号:465-454-427)
邀 请 人：孙亮亮 副教授
届时欢迎广大师生参与交流！
报告人简介
闫亮，东南大学副教授、博士生导师，中国工业与应用数学学会不确定性量化专业委员会常务委员。主要从事不确定性量化、贝叶斯反问题理论与算法的研究。2018年入选东南大学“至善青年学者”（A层次）支持计划，2017年入选江苏省高校“青蓝工程”优秀青年骨干教师培养对象。目前主持国家自然科学基金面上项目一项，主持完成国家自然科学基金面上项目和青年项目各一项。已经在《SIAM J. Sci. Comput.》、《Inverse Problems》、《J. Comput. Phys.》等国内外刊物上发表30多篇学术论文。
