# Active Subspace Methods for Data-Intensive Inverse Problems

## 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.

- Authors:

- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

- Publication Date:

- Research Org.:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

- OSTI Identifier:
- 1353429

- Report Number(s):
- DE-SC0011089

- DOE Contract Number:
- SC0011089

- Resource Type:
- Technical Report

- Country of Publication:
- United States

- Language:
- English

- Subject:
- 97 MATHEMATICS AND COMPUTING

### Citation Formats

```
Wang, Qiqi.
```*Active Subspace Methods for Data-Intensive Inverse Problems*. United States: N. p., 2017.
Web. doi:10.2172/1353429.

```
Wang, Qiqi.
```*Active Subspace Methods for Data-Intensive Inverse Problems*. United States. doi:10.2172/1353429.

```
Wang, Qiqi. Thu .
"Active Subspace Methods for Data-Intensive Inverse Problems". United States.
doi:10.2172/1353429. https://www.osti.gov/servlets/purl/1353429.
```

```
@article{osti_1353429,
```

title = {Active Subspace Methods for Data-Intensive Inverse Problems},

author = {Wang, Qiqi},

abstractNote = {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.},

doi = {10.2172/1353429},

journal = {},

number = ,

volume = ,

place = {United States},

year = {Thu Apr 27 00:00:00 EDT 2017},

month = {Thu Apr 27 00:00:00 EDT 2017}

}

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