├── .github └── workflows │ └── deploy.yml ├── .gitignore ├── .pre-commit-config.yaml ├── LICENSE ├── README.md ├── _config.yml ├── _toc.yml ├── bibliography.md ├── conda-linux-64.lock ├── environment.yaml ├── fossil ├── DA_methods.py └── README.md ├── intro.md ├── newlogo.png ├── notebooks ├── B_clim_L96.npy ├── DA_demo_L96.ipynb ├── DA_methods.py ├── GP_lorenz96.ipynb ├── Improving_Neural_networks.ipynb ├── L96-two-scale-description.ipynb ├── L96_ResNet_RNN.ipynb ├── L96_conservative_model.py ├── L96_model.py ├── L96_offline_training_NN.ipynb ├── L96_online_implement_NN.ipynb ├── L96_online_training_NN.ipynb ├── Learning-DA-increments.ipynb ├── Neural-Network-Interpretation.ipynb ├── citing.ipynb ├── closing_remarks.ipynb ├── constraints.ipynb ├── estimating-gcm-parameters.ipynb ├── figs │ ├── .DS_Store │ ├── Burgers_simulation.gif │ ├── CNN22.svg │ ├── Classification.png │ ├── DA_step.png │ ├── Deeper_network.svg │ ├── Dropout_layer.png │ ├── Gradient_descent2.png │ ├── L96_one_variable.gif │ ├── Learning_curve.png │ ├── LogReg_NN.png │ ├── Log_reg_NN.jpg │ ├── Log_reg_NN.png │ ├── Loss_function_vis_NN.jpeg │ ├── Regression.png │ ├── Transfer_learning.png │ ├── cyclic_lr.png │ ├── diagram_CNN.png │ ├── gp_mutation_types.png │ ├── linear_regression_as_neural_network.png │ ├── linear_regression_as_neural_network2.png │ ├── local_map.png │ ├── local_map_ANN.png │ ├── local_map_polynet.png │ ├── maxpool.jpeg │ ├── overfitting.png │ └── relu_activation_function.png ├── gcm-analogue.ipynb ├── gcm-parameterization-problem.ipynb ├── intro_ML_and_NNs.ipynb ├── local_map_L96_and_Burgers.ipynb ├── networks │ └── network_3_layers_100_epoches.pth ├── old_notebooks │ ├── L96_online_implement_NN_old.ipynb │ ├── Neural-Network-Advection.ipynb │ ├── feature_importance.ipynb │ ├── gcm-analogue-old.ipynb │ ├── gcm-parameterization-problem-old.ipynb │ └── gradient_decent-old.ipynb ├── random_forest_parameterization.ipynb ├── rvm.py ├── sindy_L96_2scale.ipynb ├── symbolic_methods_comparison.ipynb ├── symbolic_vs_nn_multiscale_L96.ipynb └── utils.py ├── paper.bib ├── paper.md ├── pdf ├── M2LInES L96 DA Demo.pdf ├── M2LInES-L96-learning-from-DA-Slides.pdf ├── README.md ├── m2lines-202108-calibml.pdf ├── ml_methods_slides.pdf └── presentation-model-setup.pdf ├── references.bib └── requirements.txt /.github/workflows/deploy.yml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/m2lines/L96_demo/HEAD/.github/workflows/deploy.yml -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/m2lines/L96_demo/HEAD/.gitignore -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 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