Bayesian inference of stochastic dynamical models
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2013
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| Online Access: | http://hdl.handle.net/1721.1/79265 |
| _version_ | 1826198884743905280 |
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| author | Lu, Peter Guang Yi |
| author2 | Pierre F.J. Lermusiaux. |
| author_facet | Pierre F.J. Lermusiaux. Lu, Peter Guang Yi |
| author_sort | Lu, Peter Guang Yi |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013. |
| first_indexed | 2024-09-23T11:11:11Z |
| format | Thesis |
| id | mit-1721.1/79265 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:11:11Z |
| publishDate | 2013 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/792652019-04-10T22:01:40Z Bayesian inference of stochastic dynamical models Lu, Peter Guang Yi Pierre F.J. Lermusiaux. Massachusetts Institute of Technology. Department of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering. Mechanical Engineering. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (p. 165-175). A new methodology for Bayesian inference of stochastic dynamical models is developed. The methodology leverages the dynamically orthogonal (DO) evolution equations for reduced-dimension uncertainty evolution and the Gaussian mixture model DO filtering algorithm for nonlinear reduced-dimension state variable inference to perform parallelized computation of marginal likelihoods for multiple candidate models, enabling efficient Bayesian update of model distributions. The methodology also employs reduced-dimension state augmentation to accommodate models featuring uncertain parameters. The methodology is applied successfully to two high-dimensional, nonlinear simulated fluid and ocean systems. Successful joint inference of an uncertain spatial geometry, one uncertain model parameter, and [Omicron](105) uncertain state variables is achieved for the first. Successful joint inference of an uncertain stochastic dynamical equation and [Omicron](105) uncertain state variables is achieved for the second. Extensions to adaptive modeling and adaptive sampling are discussed. by Peter Lu. S.M. 2013-06-17T19:51:45Z 2013-06-17T19:51:45Z 2013 2013 Thesis http://hdl.handle.net/1721.1/79265 846627771 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 175 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Mechanical Engineering. Lu, Peter Guang Yi Bayesian inference of stochastic dynamical models |
| title | Bayesian inference of stochastic dynamical models |
| title_full | Bayesian inference of stochastic dynamical models |
| title_fullStr | Bayesian inference of stochastic dynamical models |
| title_full_unstemmed | Bayesian inference of stochastic dynamical models |
| title_short | Bayesian inference of stochastic dynamical models |
| title_sort | bayesian inference of stochastic dynamical models |
| topic | Mechanical Engineering. |
| url | http://hdl.handle.net/1721.1/79265 |
| work_keys_str_mv | AT lupeterguangyi bayesianinferenceofstochasticdynamicalmodels |