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Scientific Computing Research Environments for the Mathematical Sciences

Scientific Computing Research Environments for the Mathematical Sciences
数学科学的科学计算研究环境
批准号:
0215392
负责人:
Jinchao Xu
金额:
$10.03万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2002
资助国家:
美国
项目状态:
已结题
起止时间:
2002-08-15 至 2004-07-31

项目摘要

项目成果

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中文摘要
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英文摘要
NSF proposal DMS-0215392PIs: Xu, Belmonte, Du, Li and ZikatanovABSTRACTThe Department of Mathematics at the Pennsylvania StateUniversity will purchase a 64-node parallel PC clusterto be dedicated to the support of research and teachingin the mathematical sciences. In particular, the PCcluster will be used to support the research projectsof faculty members in the areas of the numericalsolution of partial differential equations in fluiddynamics and material sciences, and computationalfinance and in the studies of general numericaltechniques such as parallel multigrid algorithms andquasi-Monte Carlo methods. In particular, the projectsinclude studies of important issues concerning modelingand simulations of non-Newtonian flows, liquid crystals,quantized vortices, water waves and fuel cells. Much ofthe research efforts rely critically on theestablishment of the proposed PC cluster.The proposal involves an integrated collaborationbetween the numerical work to be performed in the PCcluster and the experimental work to be performed inthe W. G. Pritchard Laboratories of the Department ofMathematics. The new equipment will make it possible tonumerically simulate the various complex physicalphenomena observed in the fluid lab and will greatlyenhance collaborations among researchers in computationaland applied mathematics at Penn State. The PC clusterwill also be the basis for the creation of a newcomputational laboratory, which together with thePritchard fluid lab, will provide a unique environmentfor multidisciplinary research as well as for (bothundergraduate and graduate) student training.
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会议论文
Workshop on Mathematical Machine Learning and Application
US Participation at the Twenty-sixth Internaltional Domain Decomposition Conference
Multigrid Methods and Machine Learning
Integrated Geometric and Algebraic Multigrid Methods
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