Symplectic model reduction methods for the Vlasov equation, Tomasz M. Tyranowski, Michael Kraus,
lien.
Stochastic variational principles for the collisional Vlasov-Maxwell and Vlasov-Poisson equations , Tomasz M. Tyranowski,
Proceedings of the Royal Society A 477 (2252), 20210167, lien.
Publications
Publications de journaux et preprint
A neural network closure for the Euler-Poisson system based on kinetic simulations, L. Bois, E. Franck, L. Navoret, V. Vigon,
"Kinetic and related models", Février 2022, 15(1): 49-89, lien.
Data-driven structure-preserving model reduction for stochastic Hamiltonian systems , Tomasz M. Tyranowski,
Preprint lien.
Reduced order modeling using auto-encoder and Hamiltonian neural networks ,
R. Côte, E. Franck, L. Navoret, V. Vigon, G. Steimer, Preprint lien
Generalizing Adam To Manifolds For Efficiently Training Transformers ,
B. Brantner, Preprint lien
Symplectic Autoencoders for Model Reduction of Hamiltonian Systems ,
B. Brantner, M. Kraus, Preprint lien
Proceedings
Hyperbolic reduced model for Vlasov-Poisson equation with Fokker-Planck collision, E. Franck, I. Lannabi,
Y. Nasseri, L. Navoret, G. Parasiliti Rantone and G. Steimer, lien.
Structure-Preserving Transformers for Learning Parametrized Hamiltonian Systems ,
B. Brantner, G. de Romemont, M. Kraus, Z. Li, Preprint lien
Stages
Stage de M1 et M2
Claire Schnoebelen, Modèles réduits pour des EDP paramètriques, lien
Thèses et Habilitation
Thèses
Habilitation de recherche
Emmanuel Franck: Numerical method for stiff conservation laws. Application to gas dynamics and plasma physics, Janvier 2023.
