Stochastic processes insights from VAE. Code for the paper: Learning minimal representations of stochastic processes with variational autoencoders.

This repository contains Python (Jupyter Notebooks), C and Shell code, which was used to generate figures in a paper under the same name.

KISTEP modular pipeline for the characterization of anomalous diffusion trajectories.

Codes and instructions to replicate the research published in Cobarrubia et al. Frontiers in Physics 2021.

First explicit curvature correction formula for fractional Laplacians on curved manifolds. Complete proof via heat kernel expansion, validated computationally on S². Applications in anomalous diffusion, thermal field theory, and curved spacetime physics.

Regime diagnostics for spatiotemporal kernels K(r,t), 1D-4D: transport × coherence classification, isotropic S(k,ω) via Hankel/spherical-Bessel, plus stateflow — a domain-agnostic trajectory layer. NumPy/SciPy only. pip install kernel-dynamics-viewer

Exploration of voter model with power law time-dependent event rates

3D Slicer extension that provides several approaches in order to apply the anomalous spatial filters on medical images.

This repository contains the code for the analysis reported in Physical Review E 96, 022417.

Dynamic Weighted Fractional Entropy for Time-Fractional Diffusion Processes via Moment Formulas