François Gay-Balmaz
Lie-Poisson Neural Networks (LPNets): Data-based computing of Hamiltonian systems with symmetries.
Lie-Poisson Neural Networks (LPNets): Data-based computing of Hamiltonian systems with symmetries.
F. Gay-Balmaz is an Associate Professor in Mathematics at NTU Singapore since 2023. Previously he was a researcher at the Centre National de la Recherche Scientifique (CNRS, France) at Ecole Normale Supérieure de Paris (ENS). He received his Master (2004) and Ph.D. degrees (2009) from the Swiss Federal Institute of Technology (EPFL, Switzerland) and his Habilitation (2018) at Sorbonne University. He was also a PostDoc at EPFL and at the California Institute of Technology (2009-2010). His research focuses on the development of structure-preserving methods for the modeling and discretization of partial differential equations arising in fluid dynamics and nonlinear elasticity. His approach is based on tools derived from differential geometry, symplectic and Poisson geometry, and geometric mechanics. His recent interests include geophysical fluid dynamics and nonequilibrium thermodynamics.