NSF East Asia and Pacific Summer Institute (EAPSI) for FY 2013 in China

该行动资助德克萨斯大学奥斯汀分校的Christina Frederick于2013年夏天在中国科学院北京计算数学与科学与工程计算研究所开展一项数学与物理科学领域的研究项目。项目名称是“使用异构多尺度方法发现有效系统中的精细尺度特征”。主持会议的科学家是明平平博士。在为科学和工程中出现的复杂过程设计模型时，一项具有挑战性的任务是使用计算成本低廉的数值方法在大范围的空间或时间尺度上捕获特征。最近发展起来的异质多尺度方法（HMM）为这种数值方法的设计提供了一个框架，通过结合局部微尺度计算来增强现有模型。这个项目颠倒了这个过程，并分析了HMM在从给定的粗尺度数据中恢复系统的微尺度特征方面的使用。EAPSI奖学金的广泛影响包括为研究员提供美国以外的第一手研究经验；介绍该地区的科学、科学政策和科学基础设施；以及对社会、文化和语言的了解。这些活动符合美国国家科学基金会的目标，即在其科学家、工程师和教育工作者的职业生涯早期培养国际合作，从而确保美国科学劳动力具有全球意识。此外，本研究可以应用于广泛学科的逆问题，包括地球物理学、医学成像和材料科学。

This action funds Christina Frederick of The University of Texas at Austin to conduct a research project in the Mathematical and Physical Sciences area during the summer of 2013 at the Institute of Computational Mathematics and Scientific and Engineering Computing of Chinese Academy of Sciences in Beijing, China. The project title is "Discovering Fine Scale Features in Effective Systems Using Heterogeneous Multiscale Methods." The host scientist is Dr. Pingbing Ming.A challenging task in the design of models for the complex processes that arise in science and engineering is capturing features at a wide range of spatial or temporal scales with numerical methods that are computationally inexpensive. The recently developed Heterogeneous Multiscale Methods (HMM) provide a framework for the design of such numerical methods that enhance current models by incorporating local microscale calculations. This project reverses the process and analyzes the use of HMM in recovering microscale features of a system from given coarse scale data.Broader impacts of an EAPSI fellowship include providing the Fellow a first-hand research experience outside the U.S.; an introduction to the science, science policy, and scientific infrastructure of the respective location; and an orientation to the society, culture and language. These activities meet the NSF goal to educate for international collaborations early in the career of its scientists, engineers, and educators, thus ensuring a globally aware U.S. scientific workforce. Furthermore, this proposed research can be applied to inverse problems in a wide range of disciplines including geophysics, medical imaging, and materials science.