From the 1 of 5.5k papers with an AI index.
- Centre National de la Recherche ScientifiqueFR1.7k papers
- Centre Inria de SaclayFR364 papers
- École PolytechniqueFR306 papers
- Laboratoire Lorrain de Recherche en Informatique et ses ApplicationsFR300 papers
- École Normale Supérieure - PSLFR292 papers
- Sorbonne UniversitéFR291 papers
- École Normale Supérieure de LyonFR233 papers
- Centre Inria de l'Université Grenoble AlpesFR216 papers
- Université Paris-SaclayFR208 papers
- Université Grenoble AlpesFR204 papers
- Lyon 1 UniversitéFR191 papers
- Centre Inria d'Université Côte d'AzurFR183 papers
13 papers · 2 filters
Reduced-order modeling for cardiac electrophysiology. Application to parameter identification
Muriel Boulakia, Elisa Schenone, Jean-Frédéric Gerbeau
A reduced-order model based on Proper Orthogonal Decomposition (POD) is proposed for the bidomain equations of cardiac electrophysiology. Its accuracy is assessed through electroca…
Counting eigenvalues in domains of the complex field
Emmanuel R. Kamgnia, Bernard Philippe
A procedure for counting the number of eigenvalues of a matrix in a region surrounded by a closed curve is presented. It is based on the application of the residual theorem. The qu…
A new model for shallow viscoelastic fluids
François Bouchut, Sébastien Boyaval
We propose a new reduced model for gravity-driven free-surface flows of shallow elastic fluids. It is obtained by an asymptotic expansion of the upper-convected Maxwell model for e…
On the concentration properties of Interacting particle processes
Pierre Del Moral, Peng Hu, Liming Wu
These lecture notes present some new concentration inequalities for Feynman-Kac particle processes. We analyze different types of stochastic particle models, including particle pro…
The Cosparse Analysis Model and Algorithms
Sangnam Nam, Mike E. Davies, Michael Elad +1
After a decade of extensive study of the sparse representation synthesis model, we can safely say that this is a mature and stable field, with clear theoretical foundations, and ap…
Convergence Rate of the Causal Jacobi Derivative Estimator
Da-Yan Liu, Olivier Gibaru, Wilfrid Perruquetti
Numerical causal derivative estimators from noisy data are essential for real time applications especially for control applications or fluid simulation so as to address the new par…