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Scientific Reinforcement Learning for sub-grid scale modeling of 2D turbulence

[project website]

Table of contents

Introduction

The checklist for refactor progress

  • Check consistency with Py2D
    • RHS
    • Forcing: FK
    • CN for $\Pi$ formulation psiTemp = RHS/(1+dt*alpha+0.5*dt*(nu+ve)*Ksq)
    • Check operators and grid [ij] vs [xy] (all in [ij])
      • meshgrid Kx, Ky
      • meshgrid xx, yy
      • Checked $u,v$ calculations, and added a function: psi_2_uv
    • Change $\beta*v$ term to match the Py2D convention: changed with 'ij' indexing grid
  • Option for calculation of $\Pi$
    • From $\sigma$
    • From $\tau$
  • Optional CPU-GPU backend
  • Consistent model action ($c_l^3$ and $c_s^2$)
  • Options to save a list of parameters ($\omega$, $\psi$, $\nu_e$, $c_{model}$, $\Pi$, action list)
  • Re-organize the state model, maybe have it as a list of options (accumalative):
    • Global: energy spectra
    • Global: enstrophy spectra
    • local: $\nabla u$
    • local: $\nabla \nabla u$
    • choice of invariants $\lambda_i$
  • Update initial condition for cases (and the corresponding spectra)
    • Case 1: $\kappa_f=4$ , Re$=20\times10^3$, $\beta=0$
    • Case 2: $\kappa_f=4$ , Re$=20\times10^3$, $\beta=0$
    • Case 3: $\kappa_f=25$ , Re$=20\times10^3$, $\beta=20$
  • Check consistency of IC mat files with the solver
  • Case management system: Copy config file in the folder
  • bring options to sh file:
    • ["Policy"]["Distribution"] choice
  • Retrain with a [super-]gaussian spectra (to have a better behaving interscale transfers )
  • Code to track interscale variation while training
  • Double check the naming of time steps: n_init, ...

Requirements

Experiments

To run the training

bash run.sh

To run post process

bash runpost.sh

To delete all data files in the folder

bash clean.sh

Citation

  • Mojgani, R., Waelchli, D., Guan, Y., Koumoutsakos, P., Hassanzadeh, P. "Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence", arXiv: 2312.00907, 2023.(url)
    BibTeX
    @article{Mojgani_arxiv_2023,
title={Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence},
author={Rambod Mojgani and Daniel Waelchli and Yifei Guan and Petros Koumoutsakos and Pedram Hassanzadeh},
year={2023},
eprint={2312.00907},
archivePrefix={arXiv},
}