# Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice

- Authors:

- Publication Date:

- Sponsoring Org.:
- USDOE

- OSTI Identifier:
- 1329337

- Grant/Contract Number:
- AC05-76RL01830

- Resource Type:
- Journal Article: Publisher's Accepted Manuscript

- Journal Name:
- Journal of Computational Physics

- Additional Journal Information:
- Journal Volume: 321; Journal Issue: C; Related Information: CHORUS Timestamp: 2017-10-06 09:02:51; Journal ID: ISSN 0021-9991

- Publisher:
- Elsevier

- Country of Publication:
- United States

- Language:
- English

### Citation Formats

```
Li, Weixuan, Lin, Guang, and Li, Bing.
```*Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice*. United States: N. p., 2016.
Web. doi:10.1016/j.jcp.2016.05.040.

```
Li, Weixuan, Lin, Guang, & Li, Bing.
```*Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice*. United States. doi:10.1016/j.jcp.2016.05.040.

```
Li, Weixuan, Lin, Guang, and Li, Bing. Thu .
"Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice". United States.
doi:10.1016/j.jcp.2016.05.040.
```

```
@article{osti_1329337,
```

title = {Inverse regression-based uncertainty quantification algorithms for high-dimensional models: Theory and practice},

author = {Li, Weixuan and Lin, Guang and Li, Bing},

abstractNote = {},

doi = {10.1016/j.jcp.2016.05.040},

journal = {Journal of Computational Physics},

number = C,

volume = 321,

place = {United States},

year = {Thu Sep 01 00:00:00 EDT 2016},

month = {Thu Sep 01 00:00:00 EDT 2016}

}

Free Publicly Available Full Text

Publisher's Version of Record at 10.1016/j.jcp.2016.05.040

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Cited by: 5works

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