Discovering PDEs from Multiple Experiments In this repo, we share the code, data and results of the paper (https://arxiv.org/abs/2109.11939) (1) To promote grouped sparsity: we propose and implement a randomized adaptive group Lasso with stability selection and error control see /pdeX/sparsity_estimators.py (2) Deep learning based model discovery: we implement the latter sparsity estimator in DeepMod (that we extend to handle multiple experiments) we leverage JAX to perform backward and forward autodiffs see /pdeX/DeepModx.py (3) We share the code to reproduce the numerical experiments: varying parameters (paramsXX.ipynb), varying initial conditions (ICs_XX.ipynb) and different chaotic regimes (chaos_XX.ipynb) where XX = {GL: randomized adaptive Group Lasso (grouped sparsity) or IL: randomized adaptive Lasso (individual sparsity)} Requirements: conda and pip requirements are shared (see .txt files)