phase-transitions

A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...

Python package for automated bifurcation analysis and parameter continuations, based on Auto-07p.

lindemann is a python package to calculate the Lindemann index of a lammps trajectory

Mathematica package for evaluation of the bounce action with multiple scalar fields

PhaseTracer is a C++14 software package for tracing cosmological phases, finding potential phase transitions, computing the bounce action, and plotting the gravitational wave spectrum for Standard Model extensions with any number of scalar fields

Tracking exactly what happens to the internal "circuitry" (induction heads) of a 2-layer attention-only Transformer when forced to undergo domain adaptation from prose to structured Python code.

Software for modeling phase transitions in the early universe using the Sound Shell Model

The emergence of eukaryotes as an evolutionary algorithmic phase transition

Toolkit to study Cosmological Phase Transitions + Primordial Black Holes + Gravitational Waves

This repository contains the code to reproduce the experiments performed in the Dynamical Mean-Field Theory of Self-Attention Neural Networks article.

The code for my PRE article titled "Persistent Homology for Structural Characterization in Disordered Systems"

Studying the Ising model phase transition using PCA.

Polynomial filtering eigensolver and Hamiltonian tools for quantum many-body simulations in Julia.

Investigating cluster dynamics in models of patchy ecosystems

CUDA Vicsek model simulator

Detecting phase transitions from Quantum Monte Carlo datasets using the "learning by confusion" (LbC) technique. Training CNNs in PyTorch to classify phases of matter in an electron-phonon model.

Numerical simulation and theoretical analysis of Galton-Watson branching processes in Julia, covering extinction probabilities, phase transitions, carrying capacity constraints, and multi-type population dynamics.

Adaptive Intelligence Framework (AIF) formalizes intelligence as a boundary-regulated adaptive process. Grounded in SymC principles, it studies how systems maintain coherence under uncertainty through constraint negotiation, regulation, and phase transitions - focusing on adaptive stability rather than benchmark optimization or ML scaling.

A demo to simulate 2D spin lattices with different shapes, boundary conditions, models and algorithms

Tracing the links between Statistical Mechanics and AI. Phase 1 features a vectorized 2D Ising Model simulation. Phase 2 maps these dynamics to Hopfield Networks to show how physical energy minimization drives memory recall.