報告題目： Failure-informed adaptive sampling for PINNs
報 告 人：闫亮 副教授 單位名稱 東南大學
報告時間：2023年5月5日 15:30-16:30
報告地點：騰訊會議 ID：500-921-275
會議鍊接：https://meeting.tencent.com/dm/yfHVaEcmzUWg
校内聯系人：刁懷安 diao @ jlu.edu.cn
報告摘要： Physics-informed neural networks (PINNs) have emerged as an effective technique for solving PDEs in a wide range of domains. Recent research has demonstrated, however, that the performance of PINNs can vary dramatically with different sampling procedures, and that using a fixed set of training points can be detrimental to the convergence of PINNs to the correct solution. In this talk, we present an adaptive approach termed failure-informed PINNs(FI-PINNs), which is inspired by the viewpoint of reliability analysis. The basic idea is to define a failure probability by using the residual, which represents the reliability of the PINNs. With the aim of placing more samples in the failure region and fewer samples in the safe region, FI-PINNs employs a failure-informed enrichment technique to incrementally add new collocation points to the training set adaptively. When compared to the conventional PINNs method and the residual-based adaptive refinement method, the developed algorithm can significantly improve accuracy, especially for low regularity and high-dimensional problems.
報告人簡介：闫亮，副教授、博士生導師。主要從事不确定性量化、貝葉斯反問題理論與算法的研究。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，2018年入選東南大學首批“至善青年學者”（A層次）支持計劃。2019年在全國反問題年會上獲得“優秀青年學術獎”。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。
