1 citations · 2 across the 8 of their papers we have counts for
8 papers
Reduced Order Modeling and Applications to Inverse State Estimation
Olga Mula, Agustín Somacal
Solving parametric partial differential equations (PDEs) repeatedly for many parameter values,as needed in optimal control, uncertainty quantification, and inverse problems, is oft…
Natural gradient descent with momentum
Anthony Nouy, Agustín Somacal
We consider the problem of approximating a function by an element of a nonlinear manifold which admits a differentiable parametrization, typical examples being neural networks with…
State estimation of urban air pollution with statistical, physical, and super-learning graph models
Matthieu Dolbeault, Olga Mula, Agustín Somacal
We consider the problem of real-time reconstruction of urban air pollution maps. The task is challenging due to the heterogeneous sources of available data, the scarcity of direct…
High order recovery of geometric interfaces from cell-average data
Albert Cohen, Olga Mula, Agustín Somacal
We consider the problem of recovering characteristic functions from cell-average data on a coarse grid, and where is a compact set of . This task arises…
Reduced order modeling for elliptic problems with high contrast diffusion coefficients
Albert Cohen, Matthieu Dolbeault, Agustin Somacal +1
We consider the parametric elliptic PDE on a spatial domain , with a scalar piecewise constant diffusion coefficient taking any positive val…
Nonlinear approximation spaces for inverse problems
Albert Cohen, Matthieu Dolbeault, Olga Mula +1
This paper is concerned with the ubiquitous inverse problem of recovering an unknown function u from finitely many measurements possibly affected by noise. In recent years, inversi…
Clustering high dimensional meteorological scenarios: results and performance index
Yamila Barrera, Leonardo Boechi, Matthieu Jonckheere +5
The Reseau de Transport d'Electricité (RTE) is the French main electricity network operational manager and dedicates large number of resources and efforts towards understanding cli…
Uncovering differential equations from data with hidden variables
Agustín Somacal, Yamila Barrera, Leonardo Boechi +4
SINDy is a method for learning system of differential equations from data by solving a sparse linear regression optimization problem [Brunton et al., 2016]. In this article, we pro…