报告题目: 模型预测中的不确定性分析
讲座教授：叶明
美国佛罗里达州立大学科学计算系
& 地球物理流体力学研究所副教授
主 持 人：张凡研究员
地 点：中国科学院青藏高原研究所 新楼 915 会议室
时 间：2010年5月25日(周五）15:00–17:00
来访专家简介:
叶明1997年南京大学地球科学系本科毕业，2002年美国亚利桑那大学水文及水资源系毕业并获博士学位。现为美国地球物理学会《Water Resources Research》杂志副主编，以及Interagency Steering Committee on Multimedia Environmental Models (https://iemhub.org/topics/iscmem) 第二工作组 (Working Group 2: Prediction and Uncertainty) 成员。发表学术论文三十余篇, 多次获得美国国家自然科学基金、国防部、原子能管理委员会以及佛罗里达州环保厅的资助。2012 获得美国能源部 Early Career Award.
摘 要:
Over the last four decades, numerical modeling has become a vital tool to help understand and predict complex physical, chemical, and biological processes of subsurface environmental systems. Due to lack of data and knowledge to describe the processes and their interactions, model predictions are inherently uncertain. Quantification of predictive uncertainty is critical to science-based decision-making for improving defensibility of model results and reducing cost of water resource management and environmental protection. This presentation will introduce a Bayesian method for assessment of two hierarchical types of uncertainty, i.e., parametric and conceptual model uncertainties, due to non-unique descriptions of model parameters and model structures. Assessment of parametric uncertainty is to quantify predictive uncertainty of a single model, whereas assessment of conceptual model uncertainty is to evaluate variability in model predictions, at a higher level, arising from multiple acceptable models. This presentation will introduce several applications of the Bayesian approaches to jointly quantify conceptual model and parametric uncertainties in numerical modeling of subsurface flow and solute reactive transport in saturated and unsaturated porous and fractured media.
