Ising model, machine learning and AdS/CFT

Simulation of an Ising model in AdS-CFT-correspondence

Ontogeny and pedagogy of Morphological Source Code (MSC), implemented in Python3 for contemporary hardware. Operates as a quantized kernel of agentic motility, akin to a Hilbert space kernel; augmented by an AdS/CFT-dual Noetherian jet space enabling topos-invariant syntax-lift/lower, morphological differentiation, and morphosemantic integration.

Own implementation of the EFL Network

Machine Learning Modeling for T-Linear Resistivity

Hydrodynamics with dynamical gauge fields

This repository contains python scripts for computing poincare sums for AdS3 gravity and RCFT partition functions using the SAGE Math.

Exploration théorique de l'isomorphisme entre la géométrie hyperbolique des réseaux complexes (TPDBT), la dualité AdS/CFT et le routage glouton optimal. Une approche unifiée de la topologie de l'information.

Cadre théorique formel visant à unifier la thermodynamique, la théorie de l’information et la gravitation émergente à travers la géométrie informationnelle. Le projet explore la courbure thermodynamique de Ruppeiner ($R$) comme invariant géométrique encodant la microstructure, les corrélations et les interactions fondamentales des systèmes physique

Holographic time mapping protocol (v → T → τ) - TDHCF framework documentation

📐 Explore efficient routing in complex networks through hyperbolic geometry, revealing connections between information structures and the universe's fabric.