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年夏天在中国北京的中国科学院计算数学与科学与工程计算研究所进行数学和物理科学领域的研究项目。该项目的名称是“使用异构多尺度方法在有效系统中发现精细尺度特征”。“主持科学家是Ming Pingbing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用计算成本低的数值方法在广泛的空间或时间尺度上捕获特征。最近开发的非均质多尺度方法（HMM）为此类数值方法的设计提供了一个框架，这些方法通过结合局部微尺度计算来增强当前模型。该项目反向的过程，并分析使用隐马尔可夫模型从给定的粗尺度数据恢复系统的微尺度特征。EAPSI奖学金的更广泛的影响包括提供研究员在美国以外的第一手研究经验;介绍各自所在地的科学，科学政策和科学基础设施;以及社会，文化和语言的方向。这些活动符合NSF的目标，即在科学家，工程师和教育工作者的职业生涯早期进行国际合作教育，从而确保具有全球意识的美国科学工作者。此外，这项研究可以应用于反问题，在广泛的学科，包括电子物理学，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.