Replication Data for: Super-resolution of turbulent velocity fields in two-way coupled particle-laden flows

Particle-free training and test datasets (used in Fig 6 of the paper): Particle-free training dataset: data_train_OF-f0_FHIT_particle_128_Re52_prolonged-2D_102400_lmdb.lmdb Particle-free test dataset: data_test_outOfSample_OF-f0_FHIT_particle_128_Re52_prolonged-2D_800_lmdb.lmdb Out of sample test datasets: Test Case4 in the paper: data_test_outOfSample_OF-f41_FHIT_particle_128_Re52_test-2D_800_lmdb.lmdb Test Case5 in the paper: data_test_outOfSample_OF-f51_FHIT_particle_128_Re52_test-2D_800_lmdb.lmdb experiments The trained models are provided in experiments.tar.gz file.

Each experiment contains the log file of the training, the last training state (for restart) and the model wights used in the publication. Conditional model: conditionalSRGAN trained model using particle-free dataset (used in Figs 6 and 7 of the paper): 00110-01G_PFT-NoPrt_ArchT_condSRGANModel_L64SP4x_Gcond_WaveDisc_f256g128b16_I64_BS16x2_Pix1-Grada-Adva_LrG45D5_fixedLR_DS-f0-102k_cPad_20241218 conditionalSRGAN trained model using the main dataset (used in Figs 9-13 and Figs 15-16 of the paper): 01004-00H_PFT-Prt_ArchTest_condSRGANModel_L64SP4x_Gcond_WaveDisc_f256g128b16_I64_BS32x4_Pix1-Grada-Adva_LrG45D5_fixedLR_DS-fxD-320k_cPad_20241219 Traditional model: unconditional SRGAN model trained model using the main dataset (used in Fig 14 of the paper): 01005-00H_PFT-Prt_DiscTest_condSRGANModel_L64SP4x_Gcond_TradDisc_f256g128b16_I64_BS32x4_Pix1-Grada-Adva_LrG45D5_fixedLR_DS-fxD-320k_cPad_20241224 How to Build the environment To build the environment required for the training and inference you need Anaconda.

Go to the torch_code folder and conda env create -f environment.yml Then create ipython kernel for post processing, conda activate torch_22_2025_Shamooni_POF python -m ipykernel install --user --name ipyk_torch_22_2025_Shamooni_POF --display-name "ipython kernel for post processing of POF2025" Perform training It is suggested to create softlinks to the dataset directly in the torch_code folder: cd torch_code ln -s <path to the dataset folder> datasets Then activate the conda environment conda activate torch_22_2025_Shamooni_POF An example script to run on single node with 2 GPUs: torchrun --standalone --nnodes=1 --nproc_per_node=2 train.py -opt options/train/condSRGAN/00110-01G_PFT-NoPrt_ArchT.yml --launcher pytorch Make sure that the paths to datasets "dataroot_gt" and "meta_info_file" for both training and validation data in option files are set correctly.