讲座人简介：

陈洁，西交利物浦大学副教授，博士生导师，分别于2004年和2007年在南京大学数学系获得学士和硕士学位，2011年在南洋理工大学获得博士学位；于2011/08-2012/10在香港科技大学做博士后研究，于2012/11-2013/03在沙特国王大学做博士后研究；2013/04进入西安交通大学yl23411永利官网工作，历任讲师、副教授；2019/08进入西交利物浦大学工作。研究方向包括有限元方法，计算流体力学，油藏模拟。在Mathematics of Computation, Journal of computational physics等国际权威期刊上发表论文四十余篇，主持和参与面上项目、青年基金、博士后特等资助、CMG基金，苏州工业园区重点研发项目等十余项科研项目。指导的员工被帝国理工学院、伦敦大学学院、宾夕法尼亚大学、荷兰国家数学与计算机科学研究中心等高校和研究机构录取；指导的博士生任教于永利集团、西北大学、烟台大学等高校。担任教育部学位论文评审专家。2019年，陈洁参加第8届华人数学家大会并做45分钟邀请报告。

讲座简介：

Modeling subsurface fluid flow in porous media is crucial for applications such as oil and gas exploration. However, the inherent heterogeneity and multi-scale characteristics of these systems pose significant challenges in accurately reconstructing fluid flow behaviors. To address this issue, we proposed Fourier Preconditioner-based Hierarchical Multiscale Net (FP-HMsNet), an efficient hierarchical preconditioner-learner architecture that combines Fourier Neural Operators (FNO) with multi-scale neural networks to reconstruct multi-scale basis functions of high-dimensional subsurface fluid flow. Using a dataset comprising 102,757 training samples, 34,252 validation samples, and 34,254 test samples, we ensured the reliability and generalization capability of the model. Experimental results showed that FP-HMsNet achieved an MSE of 0.0036, an MAE of 0.0375, and an R2 of 0.9716 on the testing set, significantly outperforming existing models and demonstrating exceptional accuracy and generalization ability. Additionally, robustness tests revealed that the model maintained stability under various levels of noise interference. Ablation studies confirmed the critical contribution of the preconditioner and multi-scale pathways to the model's performance. Compared to current models, FP-HMsNet not only achieved lower errors and higher accuracy but also demonstrated faster convergence and improved computational efficiency, establishing itself as the state-of-the-art (SOTA) approach. This model offers a novel method for efficient and accurate subsurface fluid flow modeling, with promising potential for more complex real-world applications.