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DOE OSTI · 1353429

Active Subspace Methods for Data-Intensive Inverse Problems

Wang, Qiqi
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Abstract

The project has developed theory and computational tools to exploit active subspaces to reduce the dimension in statistical calibration problems. This dimension reduction enables MCMC methods to calibrate otherwise intractable models. The same theoretical and computational tools can also reduce the measurement dimension for calibration problems that use large stores of data.

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97 MATHEMATICS AND COMPUTING

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Wang, Qiqi. 2017-04-27. Active Subspace Methods for Data-Intensive Inverse Problems. https://doi.org/10.2172/1353429

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