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ml-nonequilibrium

Code and data related to the article "Machine learning in and out of equilibrium" by Shishir Adhikari, Alkan Kabakçıoğlu, Alexander Strang, Deniz Yuret, and Michael Hinczewski

6/6/23: This repository is a work in progress: additional code and data will be made available in the near future.

The implementation and analysis of stochastic gradient descent training for the numerical examples is written in Julia, using the Knet deep learning framework. The code was tested with the Julia 1.7.2 kernel, but may run on other kernel versions.

Data files associated with the project are stored in an OSF repository.

Code:

Nonlinear regression WR: Nonlinear regression example, minibatching with replacement (WR)

Nonlinear regression WOR: Nonlinear regression example, minibatching without replacement (WOR)

Data:

Gaussian_wr_ss-30000000-1.0e-7.jld2: Sample stationary state trajectory for the nonlinear regression WR example ($3 \times 10^7$ time steps, $\eta = 10^{-7}$, Julia data format)

Gaussian_wor_ss-15000000-1.0e-7.jld2: Sample stationary state trajectory for the nonlinear regression WOR example ($1.5 \times 10^7$ epochs, batch size = 10, $\eta = 10^{-7}$, Julia data format)