GOLDEN, Colo., Jan. 20, 2014 – A Colorado School of Mines-led research team has been funded by the U.S. Department of Energy’s Advanced Scientific Computing Research program to improve the capabilities of data-intensive physical simulations such as climate modeling, groundwater flow and renewable energy applications.
The $1.05 million award dispersed over three years will allow principal investigator Paul Constantine, the Ben L. Fryrear Assistant Professor of Applied Mathematics and Statistics at Mines, along with Youssef Marzouk and Qiqi Wang of MIT and Tan Bui-Thanh of the University of Texas at Austin, to develop methods to reduce tremendous data streams into more meaningful and manageable parcels.
“One of the most important challenges is the inverse or calibration problem: find the inputs of a simulation such that its outputs agree with a given set of measurements,” said Constantine. “As both the cost of the complex simulations and the volume of measurement data sets increase, intuition-driven trial-and-error methods for calibration quickly become untenable. Scientists need more computationally efficient and mathematically rigorous methods.”
Constantine’s team will apply the methods they develop to real inverse problems in chemical kinetics and turbulent flame modeling.
“We think that newly developed active subspace methods can provide the necessary dimension reduction to help cutting edge methods find solutions for a large class of otherwise intractable statistical inverse problems,” Constantine said.
For more information, visit activesubspaces.org.
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