Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification
| dc.contributor.author | Zavar Moosavi, Azam Sadat | en |
| dc.contributor.committeechair | Sandu, Adrian | en |
| dc.contributor.committeemember | Gugercin, Serkan | en |
| dc.contributor.committeemember | Huang, Bert | en |
| dc.contributor.committeemember | Ribbens, Calvin J. | en |
| dc.contributor.committeemember | Archibald, Rick | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2018-03-14T08:00:37Z | en |
| dc.date.available | 2018-03-14T08:00:37Z | en |
| dc.date.issued | 2018-03-13 | en |
| dc.description.abstract | Simulations and modeling of large-scale systems are vital to understanding real world phenomena. However, even advanced numerical models can only approximate the true physics. The discrepancy between model results and nature can be attributed to different sources of uncertainty including the parameters of the model, input data, or some missing physics that is not included in the model due to a lack of knowledge or high computational costs. Uncertainty reduction approaches seek to improve the model accuracy by decreasing the overall uncertainties in models. Aiming to contribute to this area, this study explores uncertainty quantification and reduction approaches for complex physical problems. This study proposes several novel probabilistic and statistical approaches for identifying the sources of uncertainty, modeling the errors, and reducing uncertainty to improve the model predictions for large-scale simulations. We explore different computational models. The first class of models studied herein are inherently stochastic, and numerical approximations suffer from stability and accuracy issues. The second class of models are partial differential equations, which capture the laws of mathematical physics; however, they only approximate a more complex reality, and have uncertainties due to missing dynamics which is not captured by the models. The third class are low-fidelity models, which are fast approximations of very expensive high-fidelity models. The reduced-order models have uncertainty due to loss of information in the dimension reduction process. We also consider uncertainty analysis in the data assimilation framework, specifically for ensemble based methods where the effect of sampling errors is alleviated by localization. Finally, we study the uncertainty in numerical weather prediction models coming from approximate descriptions of physical processes. | en |
| dc.description.abstractgeneral | Computational models are used to understand the behavior of the natural phenomenon. Models are used to approximate the evolution of the true phenomenon or reality in time. We obtain more accurate forecast for the future by combining the model approximation together with the observation from reality. Weather forecast models, oceanography, geoscience, etc. are some examples of the forecasting models. However, models can only approximate the true reality to some extent and model approximation of reality is not perfect due to several sources of error or uncertainty. The noise in measurements or in observations from nature, the uncertainty in some model components, some missing components in models, the interaction between different components of the model, all cause model forecast to be different from reality. The aim of this study is to explore the techniques and approaches of modeling the error and uncertainty of computational models, provide solution and remedies to reduce the error of model forecast and ultimately improve the model forecast. Taking the discrepancy or error between model forecast and reality in time and mining that error provide valuable information about the origin of uncertainty in models as well as the hidden dynamics that is not considered in the model. Statistical and machine learning based solutions are proposed in this study to identify the source of uncertainty, capturing the uncertainty and using that information to reduce the error and enhancing the model forecast. We studied the error modeling, error or uncertainty quantification and reduction techniques in several frameworks from chemical models to weather forecast models. In each of the models, we tried to provide proper solution to detect the origin of uncertainty, model the error and reduce the uncertainty to improve the model forecast. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:14411 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/82491 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Uncertainty Quantification | en |
| dc.subject | Uncertainty Reduction | en |
| dc.subject | Stochastic Simulation of Chemical Reactions | en |
| dc.subject | Reduced-Order Models | en |
| dc.subject | Structural Uncertainty | en |
| dc.subject | Data Assimilation | en |
| dc.subject | Numerical Weather Prediction Models | en |
| dc.subject | Machine learning | en |
| dc.title | Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Computer Science and Applications | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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