学术报告

报告题目：Machine Learning and Transport Simulations for Groundwater Anomaly Detection

报告人: Liu Jiangguo教授 （科罗拉多州立大学）

报告时间: 2019年5月29日 10：30

报告地点: 数学院大会议室341

内容摘要：In this talk, we present studies on models and algorithms for groundwater anomaly detection.  Specifically, conductivity along with four other surrogates are used for identifying anomaly in groundwater, the one-class support vector machine (SVM) technique is utilized for model training, and real data from “Colorado Water Watch” is used for testing the models and algorithms.  Design of code modules in Python will be briefly discussed.  We also use data from numerical simulations of flow and transport in porous media to test the anomaly detection code modules.

报告人简介: Jiangguo Liu，美国科罗拉多州立大学数学系教授，博士生导师。曾任美国工业与应用数学学会中部地区分会主席，现任 Journal of Computational and Applied Mathematics 杂志编辑。主要研究兴趣为数值分析，科学计算，及生物数学。已在 SIAM J Numer Anal, SIAM J Sci Comput, J Comput Phys 等杂志上发表论文40多篇。