Datasets obtained from evaluation of KS-test and Caging criterion on simulation data of a dense granular gas

These files contain datasets used in the article titled "Machine learning based prediction of dynamical clustering in granular gases". Each of these files contains data described as follows: "dataset_ks_test_minimum_difference_interpolation.csv": Estimation of minimum difference from KS-test for various system configurations "test_dataset_minimum_difference_interpolation_predictions.csv": Predictions of minimum difference from KS test for various system configuration on test dataset "test_dataset_with_predictions_extrapolation_NN.csv", test_dataset_with_predictions_extrapolation_XGB.csv, and test_dataset_with_predictions_extrapolation_PR.csv contains the predictions of minimum difference from Neural Network, XGBoost and Polynomial Regression models repectively. "train_data_ks_test_extrapolation_case_2.csv" contains training data for extrapolating predictions for new amplitude "test_data_ks_test_extrapolation_case_2.csv" contains test data for extrapolating predictions for new amplitude "amplitude_test_dataset_with_predictions_extrapolation_PR.csv", "amplitude_test_dataset_with_predictions_extrapolation_XGB.csv" and "amplitude_test_dataset_with_predictions_extrapolation_NN.csv" contains predictions from Polynomial Regression, Neural Network and XGBoost regression models respectively "data_ks_test_delta_min_extrapolation_training_set_case_1.csv" contains training data for extrapolating predictions for particle number "data_ks_test_delta_min_extrapolation_test_set_case_1.csv" contains test data for extrapolating predictions for particle number "post_1500_amplitude_400_phase_diff_1_3600_7200.csv" contains simulation data for 1500 particles excited with 4 mm excitation amplitude "post_4119_amplitude_400_phase_diff_8_7_5_3600_7200.csv" contains simulation data for 4119 particles excited with 4 mm excitation amplitude