Geometric Structures of Statistical Physics, Information Geometry, and Learning SPIGL'20, Les Houches, France, July 27–31

PART 1: Tribute to Jean-Marie Souriau seminal works: G. de Saxcé and C.-M. Marle, Structure des Systèmes Dynamiques.- Jean-Marie Souriau’s book 50th birthday.-  F. Barbaresco, Jean-Marie Souriau’s Symplectic Model of Statistical Physics : Seminal papers on Lie Groups Thermodynamics - Quod Erat Demonstrandum.- PART 2: Lie Group Geometry & Diffeological Model of Statistical Physics and Information Geometry: F. Barbaresco - Souriau-Casimir Lie Groups Thermodynamics & Machine Learning.- K. Tojo and T. Yoshino, An exponential family on the upper half plane and its conjugate prior.- E. Chevallier and N. Guigui, Wrapped statistical models on manifolds: motivations, the case SE(n), and generalization to symmetric spaces.- G. de Saxcé, Galilean Thermodynamics of Continua.- H. Vân Lê and A. Tuzhilin, Nonparametric estimations and the diffeological Fisher metric.- PART 3: Advanced Geometrical Models of Statistical Manifolds in Information Geometry: J.-P. Francoise, Information Geometry and Integrable Hamiltonian Systems.- M. N. Boyom, Relevant Differential topology in statistical manifolds.- G. Pistone, A lecture about the use of Orlicz Spaces in Information Geometry.- F. Nielsen and G. Hadjeres, Quasiconvex Jensen divergences and quasiconvex Bregman divergences.- PART 4: Geometric Structures of Mechanics, Thermodynamics & Inference for Learning: F. Gay-Balmaz and H. Yoshimura, Dirac Structures and Variational Formulation of Thermodynamics for Open Systems.- A. A. Simoes, D. Martín de Diego, M. L. Valcázar and Manuel de León, The geometry of some thermodynamic systems.- F. Chinesta, E. Cueto, M. Grmela, B. Mioya, M. Pavelka and M. Sipka, Learning Physics from Data: a Thermodynamic Interpretation.- Z. Terze, V. Pandža, M. Andric and D. Zlatar, Computational dynamics of reduced coupled multibody-fluid system in Lie group setting.- F. Masi, I. Stefanou, P. Vannucci and V. Maffi-Berthier, Material modeling via Thermodynamics-based Artificial Neural Networks.- K. Grosvenor, Information Geometry and Quantum Fields.- PART 5: Hamiltonian Monte Carlo, HMC Sampling and Learning on Manifolds: A. Barp, The Geometric Integration of Measure-Preserving Flows for Sampling and Hamiltonian Monte Carlo.- A. Fradi, I. Adouani and C. Samir, Bayesian inference on local distributions of functions and multidimensional curves with spherical HMC sampling.- S. Huntsman, Sampling and Statistical Physics via Symmetry.- T. Gerald, H. Zaatiti and H. Hajri, A Practical hands-on for learning Graph Data Communities on Manifolds.