Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification

Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification

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 paramet...

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Bibliographic Details
Main Author: Zavar Moosavi, Azam Sadat
Other Authors: Computer Science
Format: Others
Published: Virginia Tech 2018
Subjects:
Uncertainty Quantification
Uncertainty Reduction
Stochastic Simulation of Chemical Reactions
Reduced-Order Models
Structural Uncertainty
Data Assimilation
Numerical Weather Prediction Models
Machine Learning
Online Access:http://hdl.handle.net/10919/82491

Internet

http://hdl.handle.net/10919/82491

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