5 citations · 8 across the 9 of their papers we have counts for
13 papers
Adaptive reduced tempering For Bayesian inverse problems and rare event simulation
Frederic Cerou, Patrick Heas, Mathias Rousset
This work proposes an adaptive sequential Monte Carlo sampling algorithm to solve Bayesian inverse problems in scenarios where likelihood evaluations are costly but can be approxim…
State-Of-The-Art Algorithms For Low-Rank Dynamic Mode Decomposition
Patrick Heas, Cedric Herzet
This technical note reviews sate-of-the-art algorithms for linear approximation of high-dimensional dynamical systems using low-rank dynamic mode decomposition (DMD). While repeati…
Generalized Kernel-Based Dynamic Mode Decomposition
Patrick Heas, Cedric Herzet, Benoit Combes
Reduced modeling in high-dimensional reproducing kernel Hilbert spaces offers the opportunity to approximate efficiently non-linear dynamics. In this work, we devise an algorithm b…
Low-rank Approximation of Linear Maps
Patrick Heas, Cedric Herzet
This work provides closed-form solutions and minimum achievable errors for a large class of low-rank approximation problems in Hilbert spaces. The proposed theorem generalizes to t…
Selecting Reduced Models in the Cross-Entropy Method
Patrick Héas
This paper deals with the estimation of rare event probabilities using importance sampling (IS), where an optimal proposal distribution is computed with the cross-entropy (CE) meth…
Reduced Modeling of Unknown Trajectories
Patrick Héas, Cédric Herzet
This paper deals with model order reduction of parametrical dynamical systems. We consider the specific setup where the distribution of the system's trajectories is unknown but the…
Optimal Low-Rank Dynamic Mode Decomposition
Patrick Héas, Cédric Herzet
Dynamic Mode Decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of non-linear systems from experimental datasets. Recently, several attempts have extende…
Model Reduction from Partial Observations
C. Herzet, P. Héas, A. Drémeau
This paper deals with model-order reduction of parametric partial differential equations (PPDE). More specifically, we consider the problem of finding a good approximation subspace…
Reduced-Order Modeling Of Hidden Dynamics
Patrick Héas, Cédric Herzet
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. This problematic arises in m…
An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model
Patrick Héas, Angélique Drémeau, Cédric Herzet
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. Our approac…
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
L. Jacques, C. De Vleeschouwer, Y. Boursier +64
The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between interna…
Self-similar prior and wavelet bases for hidden incompressible turbulent motion
Patrick Héas, Frédéric Lavancier, Souleymane Kadri-Harouna
This work is concerned with the ill-posed inverse problem of estimating turbulent flows from the observation of an image sequence. From a Bayesian perspective, a divergence-free is…
Supervised learning on graphs of spatio-temporal similarity in satellite image sequences
Patrick Héas, Mihai Datcu
High resolution satellite image sequences are multidimensional signals composed of spatio-temporal patterns associated to numerous and various phenomena. Bayesian methods have been…