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Information geometry is a study of statistical manifolds, that is, spaces of probability distributions from a geometric perspective. Its classical information-theoretic applications relate to statistical concepts such as Fisher information,…

Information Theory · Computer Science 2023-10-09 Kumar Vijay Mishra , M. Ashok Kumar , Ting-Kam Leonard Wong

We propose a unified theoretical framework for quantifying spatio-temporal interactions in a stochastic dynamical system based on information geometry. In the proposed framework, the degree of interactions is quantified by the divergence…

Neurons and Cognition · Quantitative Biology 2016-12-08 Masafumi Oizumi , Naotsugu Tsuchiya , Shun-ichi Amari

We investigate the asymptotic behavior of entropy polymatroids associated with algebraic matroids over finite fields. Given an algebraic matroid ${\sf M}:=(\mathcal{E},r)$ and the irreducible variety $V$ associated with ${\sf M}$, we…

Combinatorics · Mathematics 2025-09-22 Guillermo Matera

In this paper, we use a unified framework introduced in [3] to study two classes of nonconforming immersed finite element (IFE) spaces with integral value degrees of freedom. The shape functions on interface elements are piecewise…

Numerical Analysis · Mathematics 2018-10-19 Ruchi Guo , Tao Lin , Xu Zhang

We present an information geometric analysis of both entropic speeds and entropy production rates arising from geodesic evolution on manifolds parametrized by pure quantum states. In particular, we employ pure states that emerge as outputs…

Quantum Physics · Physics 2021-06-30 Steven Gassner , Carlo Cafaro , Sean A. Ali , Paul M. Alsing

We construct and analyze a thermodynamic extension of the recently proposed information geometric regularization of Cao and Sch\"afer. The construction extends their shock-mitigating Hessian metric geometry using the Shannon entropy to…

Numerical Analysis · Mathematics 2026-05-01 Seth Taylor , Raymond J. Spiteri , Stéphane Gaudreault

Research on the use of information geometry (IG) in modern physics has witnessed significant advances recently. In this review article, we report on the utilization of IG methods to define measures of complexity in both classical and,…

Statistical Mechanics · Physics 2018-04-10 D. Felice , C. Cafaro , S. Mancini

Entropic dynamics (ED) is a framework that allows one to derive quantum theory as a Hamilton-Killing flow on the cotangent bundle of a statistical manifold. These flows are such that they preserve the symplectic and the (information) metric…

Quantum Physics · Physics 2025-11-25 Ariel Caticha

We study the geometry of the space of densities $\VolM$, which is the quotient space $\Diff(M)/\Diff_\mu(M)$ of the diffeomorphism group of a compact manifold $M$ by the subgroup of volume-preserving diffemorphisms, endowed with a…

Differential Geometry · Mathematics 2011-05-04 Boris Khesin , Jonatan Lenells , Gerard Misiolek , Stephen C. Preston

Optimization in large language models (LLMs) unfolds over high-dimensional parameter spaces with non-Euclidean structure. Information geometry frames this landscape using the Fisher information metric, enabling more principled learning via…

Computation and Language · Computer Science 2025-12-09 Riccardo Di Sipio

A common framework for quantum mechanics, thermodynamics and information theory is presented. It is accomplished by reinterpreting the mathematical formalism of Everett's many-worlds theory of quantum mechanics and augmenting it to include…

Quantum Physics · Physics 2007-05-23 I. Devetak , A. E. Staples

Information geometry promotes an investigation of the geometric structure of statistical manifolds, providing a series of elucidations in various areas of scientific knowledge. In the physical sciences, especially in quantum theory, this…

Quantum Physics · Physics 2020-12-08 Gabriel F. Magno , Carlos H. Grossi , Gerardo Adesso , Diogo O. Soares-Pinto

We develop an information-theoretic approach to isoperimetric inequalities based on entropy dissipation under heat flow. By viewing diffusion as a noisy information channel, we measure how mutual information about set membership decays over…

Differential Geometry · Mathematics 2025-11-20 Amandip Sangha

The Fisher information metric is an important foundation of information geometry, wherein it allows us to approximate the local geometry of a probability distribution. Recurrent neural networks such as the Sequence-to-Sequence (Seq2Seq)…

Machine Learning · Statistics 2018-01-09 Alessandro Bay , Biswa Sengupta

The Expectation--Maximization (EM) algorithm is a simple meta-algorithm that has been used for many years as a methodology for statistical inference when there are missing measurements in the observed data or when the data is composed of…

Machine Learning · Statistics 2022-11-15 Hideitsu Hino , Shotaro Akaho , Noboru Murata

Recently we proposed an information entropy based method for electronic structure calculations within the density-matrix functional theory(DMFT) (Phys. Rev. Lett. 128, 013001), dubbed as $i$-DMFT. Comments have been raised regarding the…

Quantum Physics · Physics 2022-07-07 Jian Wang , Evert Jan Baerends

We propose a geometric theory of non-equilibrium thermodynamics, namely geometric thermodynamics, using our recent developments of differential-geometric aspects of entropy production rate in non-equilibrium thermodynamics. By revisiting…

Statistical Mechanics · Physics 2023-12-14 Sosuke Ito

This paper presents new geometric aspects of the behaviors of solutions to the porous medium equation (PME) and its associated equation. First we discuss the Legendre structure with information geometry on the manifold of generalized…

Statistical Mechanics · Physics 2009-12-16 Atsumi Ohara , Tatsuaki Wada

We analyze the geometry of a joint distribution over a set of discrete random variables. We briefly review Shannon's entropy, conditional entropy, mutual information and conditional mutual information. We review the entropic information…

Artificial neural networks (ANNs) are powerful tools capable of approximating any arbitrary mathematical function, but their interpretability remains limited, rendering them as black box models. To address this issue, numerous methods have…

Machine Learning · Computer Science 2024-06-11 Abhiram Anand Thiruthummal , Eun-jin Kim , Sergiy Shelyag