Now showing items 1-6 of 6
POD/DEIM reduced-order strategies for efficient four dimensional variational data assimilation
(Academic Press Inc Elsevier Science, 2015-08-15)
Efficient approximation of Sparse Jacobians for time-implicit reduced order models
Efficient Construction of Local Parametric Reduced Order Models Using Machine Learning Techniques
Reduced order models are computationally inexpensive approximations that capture the important dynamical characteristics of large, high-fidelity computer models of physical systems. This paper applies machine learning ...
Multivariate predictions of local reduced-order-model errors and dimensions
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 to these multivariate mappings as the ...
The reduced-order hybrid Monte Carlo sampling smoother
Comparison of POD reduced order strategies for the nonlinear 2D shallow water equations