Joint Structures and Common Foundation of Statistical ......Geometric Mechanics Gallilean Mechanics...

Joint Structures and Common Foundation of Statistical Physics, Information Geometry and Inference for Learning

26th July to 31st July 2020Registration, Poster Submission: https://franknielsen.github.io/SPIG-LesHouches2020/

8 Lectures (90 min)Langevin Dynamics: Old and News (x 2) – Eric Moulines Computational Information GeometryDivergence based Machine Learning – Frank NielsenNon-Parametric and Orlicz Spaces – Giovanni PistoneNon-Equilibrium Thermodynamic GeometryEvolution Equations for Open Systems - François Gay-BalmazAn Homogeneous Symplectic Approach - Arjan van der SchaftGeometric MechanicsGallilean Mechanics & Thermodynamics of Continua - Géry de SaxcéSouriau-Casimir Lie Groups Thermodynamics & Machine Learning – F. Barbaresco

15 Keynotes (60 min)SGD & Variational Inference - Pratik ChaudhariFast MCMC via Lie Group - Steve HuntsmanHMC on Symmetric/Homogeneous Spaces - Alessandro BarpExponential Familly by Representation Theory - Koichi TojoLearning Physics from Data - Francisco ChinestaInformation Geometry & Integrable Hamiltonian - Jean-Pierre FrançoiseInformation Geometry & Quantum Field - Ro JeffersonPhysical Limits to Information Processing - Susanne StillDiffeological Fisher Metric - Hông Vân LêDeep Learning as Optimal Control - Elena CelledoniDirac structures in Thermodynamics - Hiroaki YoshimuraPort Thermodynamic Systems Control - Bernhard MaschkeCovariant Momentum Map Thermodynamics - Goffredo ChircoContact Hamiltonian Systems - Manuel de LeónMultibody-Fluid System Dynamics in Lie group - Zdravko Terze

SPIGL’20