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Found 7 papers, 0 papers with code
Chilled Sampling for Uncertainty Quantification: A Motivation From A Meteorological Inverse Problem
The main contribution is to propose a general strategy, called here chilling, which amounts to sampling a local approximation of the posterior distribution in the neighborhood of a point estimate.
Non-Linear Reduced Modeling by Generalized Kernel-Based Dynamic Mode Decomposition
Reduced modeling of a computationally demanding dynamical system aims at approximating its trajectories, while optimizing the trade-off between accuracy and computational complexity.
Optimal Low-Rank Dynamic Mode Decomposition
Dynamic Mode Decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of non-linear systems from experimental datasets.
Low-Rank Dynamic Mode Decomposition: An Exact and Tractable Solution
Searching this approximation in a data-driven approach is formalised as attempting to solve a low-rank constrained optimisation problem.
Reduced-Order Modeling Of Hidden Dynamics
The objective of this paper is to investigate how noisy and incomplete observations can be integrated in the process of building a reduced-order model.
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model
In this work, we propose a novel procedure for video super-resolution, that is the recovery of a sequence of high-resolution images from its low-resolution counterpart.
Self-similar prior and wavelet bases for hidden incompressible turbulent motion
Nevertheless, the associated maximum a posteriori involves a fractional Laplacian operator which is delicate to implement in practice.
