Deep Learning for PDEs: Domain Decomposition and Adaptivity

发布时间:2023年06月02日 作者:王洪桥   阅读次数:[]

UQ&ML主题学术报告:Deep Learning for PDEs: Domain Decomposition and Adaptivity

时间:2023.6.8 16:00



Deep learning methods currently gain a lot of interest for solving partial differential equations (PDEs). However, significant challenges still exist for these new methods to achieve high accuracy, which include properly defining loss functions and choosing effective collocation points and network structures. In our work, we propose domain decomposition and adaptive procedures to improve the accuracy and efficiency of deep learning based methods.


廖奇峰, 研究员、博士生导师。博士毕业于英国曼彻斯特大学。先后在马里兰大学和MIT进行博士后研究工作。2015年加入上海科技大学信息科学学院。研究兴趣包括Model Order Reduction, Uncertainty Quantification, Numerical Methods for Partial Differential EquationsandDeeplearning等。学术成果主要发表在JCP, SISC, SIAM/ASA UQ等期刊



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