Information-Geometric Models in Data Analysis and Physics

Information geometry provides a data-informed geometric lens for understanding data or physical systems, treating data or physical states as points on statistical manifolds endowed with information metrics, such as the Fisher information. Building on this foundation, we develop a robust mathematical framework for analyzing data residing on Riemannian manifolds, integrating geometric insights into information-theoretic principles to reveal how information is structured by curvature and nonlinear manifold geometry. Central to our approach are tools that respect intrinsic geometry: gradient flow lines, exponential and logarithmic maps, and kernel-based principal component analysis. These ingredients enable faithful, low-dimensional representations and insightful visualization of complex data, capturing both local and global relationships that are critical for interpreting physical phenomena, ranging from microscopic to cosmological scales. This framework may elucidate how information manifests in physical systems and how informational principles may constrain or shape dynamical laws. Ultimately, this could lead to groundbreaking discoveries and significant advancements that reshape our understanding of reality itself.