报告时间：2023年6月13日9:30

报告地点：中以楼二层会议室

报告题目：Physics-Based and Data-Driven Modeling in Environmental Hydraulics

报告人：刘小峰 美国宾夕法尼亚州立大学长聘副教授（Tenured Associate Professor）

刘小峰，美国宾夕法尼亚州立大学土木与环境工程系长聘副教授，宾州州立大学计算与数据科学研究所、能源与环境研究所客座研究员。2000年本科毕业于清华大学水利工程专业，2003年硕士毕业于北京大学环境科学专业，2008年博士毕业于美国伊利诺伊大学厄巴纳-香槟分校（UIUC）土木工程专业。2009年至今分别在UIUC、德克萨斯大学安东尼奥分校以及宾州州立大学担任博士后、助理教授、长聘副教授。长期从事计算流体力学、泥沙输移、环境流体力学相关研究，尤其专注于环境和水资源工程问题中的数值计算模型开发和应用。从美国NSF、美国垦务局、NCHRP、战略环境研究与发展计划等机构获批项目经费累计超过430万美元，在《Geophysical Research Letters》、《Water Resources Research》、《Journal of Computational Physics》、《Coastal Engineering》、《Journal of Hydraulic Engineering》等领域内顶级期刊发表论文100余篇。现任《Journal of Hydraulic Engineering》期刊副主编。荣获2020年美国土木工程师协会（ASCE）State-Of-The-Art of Civil Engineering Award，以及2020年宾州州立大学Harry West Teaching Award。

报告摘要：

Physics-based and data-driven models are two different, yet closely related, approaches for capturing processes in real world. This talk will provide a broad overview of both approaches and then showcase some of our example projects in environmental hydraulics. Physics-based models (PBMs) solve governing equations using different numerical schemes. PBMs are founded on our knowledge of physical processes and their mathematical representations (usually in the form of partial differential equations and constitutive relationships). I will show examples models for turbulent flow and sediment transport around structures/objects. Data-driven models rely on the amount, representativeness, and quality of data to implicitly describe the physical processes. In high-dimensional space, data should be “big” enough to fully capture the input-output dynamics of a physical system. I will show examples of a deep-learning based surrogate for a 2D computational hydraulics model and its use for parameter inversion. Over time, the boundary between physics-based and data-driven models is blurred thanks to the rapid advancement in Machine Learning/AI. Hybrid approaches, such as physics-informed machine learning, have emerged. Some future research topics for environmental hydraulics will be discussed.

相关论文：

https://arxiv.org/abs/2112.10889

https://arxiv.org/abs/2203.02821

联系人：徐云成    ycxu@cau.edu.cn