Publications (5)
A Learning Based Approach for Uncertainty Analysis in Numerical Weather Prediction Models
Azam Moosavi, Vishwas Rao, Adrian Sandu
Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selec…
A Machine Learning Approach to Adaptive Covariance Localization
Azam Moosavi, Ahmed Attia, Adrian Sandu
Data assimilation plays a key role in large-scale atmospheric weather forecasting, where the state of the physical system is estimated from model outputs and observations, and is t…
Efficient Construction of Local Parametric Reduced Order Models Using Machine Learning Techniques
Azam Moosavi, Razvan Stefanescu, Adrian Sandu
Reduced order models are computationally inexpensive approximations that capture the important dynamical characteristics of large, high-fidelity computer models of physical systems…
Cluster Sampling Filters for Non-Gaussian Data Assimilation
Ahmed Attia, Azam Moosavi, Adrian Sandu
This paper presents a fully non-Gaussian version of the Hamiltonian Monte Carlo (HMC) sampling filter. The Gaussian prior assumption in the original HMC filter is relaxed. Specific…
Multivariate predictions of local reduced-order-model errors and dimensions
Azam Moosavi, Razvan Stefanescu, Adrian Sandu
This paper introduces multivariate input-output models to predict the errors and bases dimensions of local parametric Proper Orthogonal Decomposition reduced-order models. We refer…