144 citations
- Sorbonne UniversitéFR332 papers
- Centre National de la Recherche ScientifiqueFR140 papers
- Université Paris CitéFR88 papers
- Institut national de recherche en sciences et technologies du numériqueFR52 papers
- Laboratoire de Chimie ThéoriqueFR22 papers
- Centre de Recherche en Mathématiques de la DécisionFR19 papers
- Sorbonne Paris CitéFR19 papers
- École PolytechniqueFR16 papers
- Université Paris-SaclayFR16 papers
- Institut Universitaire de FranceFR15 papers
- Laboratoire des signaux et systèmesFR14 papers
- École Normale Supérieure - PSLFR13 papers
11 papers · 2 filters
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…
Meshfree Variational Physics Informed Neural Networks (MF-VPINN): an adaptive training strategy
Stefano Berrone, Moreno Pintore
In this paper, we introduce a Meshfree Variational-Physics-Informed Neural Network. It is a Variational-Physics-Informed Neural Network that does not require the generation of the…
On the quadratic stability of asymmetric Hermite basis with application to plasma physics with oscillating electric field
Ruiyang Dai, Bruno Després
We analyze why the discretization of linear transport with asymmetric Hermite basis functions can be instable in quadratic norm. The main reason is that the finite truncation of th…