144 citations
- Sorbonne UniversitéFR302 papers
- Centre National de la Recherche ScientifiqueFR117 papers
- Université Paris CitéFR75 papers
- Institut national de recherche en sciences et technologies du numériqueFR39 papers
- Centre de Recherche en Mathématiques de la DécisionFR19 papers
- Sorbonne Paris CitéFR19 papers
- Laboratoire de Chimie ThéoriqueFR18 papers
- École PolytechniqueFR15 papers
- École Normale Supérieure - PSLFR13 papers
- Département de mathématiques et applicationsFR12 papers
- Laboratoire des signaux et systèmesFR12 papers
- Université Paris-SaclayFR12 papers
36 papers · 1 filter
Genuinely multi-dimensional stationarity preserving Finite Volume formulation for nonlinear hyperbolic PDEs
Wasilij Barsukow, Mirco Ciallella, Mario Ricchiuto +1
Classical Finite Volume methods for multi-dimensional problems include stabilization (e.g.\ via a Riemann solver), that is derived by considering several one-dimensional problems i…
On the relation between Galerkin approximations and canonical best-approximations of solutions to some non-linear Schrödinger equations
Muhammad Hassan, Yvon Maday, Yipeng Wang
In this paper, we establish a superconvergence property of Galerkin approximations to some non-linear Schrödinger equations of Gross-Pitaevskii type. More precisely, denoting by $u…
A class of kernel-based scalable algorithms for data science
Philippe G. LeFloch, Jean-Marc Mercier, Shohruh Miryusupov
We present several generative and predictive algorithms based on the RKHS (reproducing kernel Hilbert spaces) methodology, which, most importantly, are scale up efficiently with la…
The velocity jump Langevin process and its splitting scheme: long time convergence and numerical accuracy
Nicolaï Gouraud, Lucas Journel, Pierre Monmarché
The Langevin dynamics is a diffusion process extensively used, in particular in molecular dynamics simulations, to sample Gibbs measures. Some alternatives based on (piecewise dete…
The lowest-order Neural Approximated Virtual Element Method on polygonal elements
Stefano Berrone, Moreno Pintore, Gioana Teora
The lowest-order Neural Approximated Virtual Element Method on polygonal elements is proposed here. This method employs a neural network to locally approximate the Virtual Element…
Nonlinear compressive reduced basis approximation for multi-parameter elliptic problem
Christophe Prud'Homme, Yvon Maday, Hassan Ballout
Reduced basis methods for approximating the solutions of parameter-dependant partial differential equations (PDEs) are based on learning the structure of the set of solutions - see…