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

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

Mathematica package for evaluation of the bounce action with multiple 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.

Simulation software for phase transitions in the early universe

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.

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.

CUDA Vicsek model simulator

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.

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

Deep Learning methods (CNNs and VAEs) applied to the study of phase transitions (of the 2d Ising Model).

Investigating cluster dynamics in models of patchy ecosystems

Probabilistic PCA for missing data: learning curves shows a phase transition and missing rate acts as an effective reduction in the signal-to-noise ratio, not the sample size.