We present a new information thermodynamics that consists of two parts with three perspectives. Part A applies the modern theory of probability in which an arbitrary convex function φ is employed as an analytic “device” to express topological dependency information. Fenchel-Young equality (FYE) establishes the notion of equilibrium through duality symmetry and the principle of maximum entropy/minimum free energy. Part B deals with a given set of repetitive measurements, where an inherent convex function emerges via the mathematics of large deviations. Maximum likelihood principle arises here and FYE provides a thermodynamic energetic narrative of recurrent data. The theory fulfills E. T. Jaynes’ vision of unifying information theory and statistical thermodynamics and bridging Kolmogorov’s mathematical theory with statistical big data modeling. This is a joint work with Bing Miao and Yong-Shi Wu,  arXiv:2312.03454.

报告人简介

钱纮，美国西雅图华盛顿大学应用数学系 Olga Jung Wan 教授。本科毕业于北京大学获得天体物理学专业，在圣路易斯华盛顿大学医学院获得生物化学和生物物理学博士学位。他于2010年当选为美国物理学会会员。钱教授的主要研究兴趣是生物系统的数学方法和物理理解，特别是在随机数学和非平衡统计物理方面。

邀请人

周海军 研究员