應學院邀請，東南大學闫亮副教授将在線作學術報告。
報告題目：Adaptive Surrogate Modeling Based on Deep Neural Networks for Bayesian Inverse Problems
報告摘要：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.
報告時間：2022年5月20日上午8:30-10:30
報告地點：騰訊會議(會議号:465-454-427)
邀 請 人：孫亮亮 副教授
屆時歡迎廣大師生參與交流！
報告人簡介
闫亮，東南大學副教授、博士生導師，中國工業與應用數學學會不确定性量化專業委員會常務委員。主要從事不确定性量化、貝葉斯反問題理論與算法的研究。2018年入選東南大學“至善青年學者”（A層次）支持計劃，2017年入選江蘇省高校“青藍工程”優秀青年骨幹教師培養對象。目前主持國家自然科學基金面上項目一項，主持完成國家自然科學基金面上項目和青年項目各一項。已經在《SIAM J. Sci. Comput.》、《Inverse Problems》、《J. Comput. Phys.》等國内外刊物上發表30多篇學術論文。
