1. Overview This is the dataset that we use for testing and training the models as desribed in the article: An approach to encode divergence-free stress fields in neural approximations based on stress potentials 2. Repository Structure data/ ├── datasets/ │ ├── grains_10_res_128_samples_5000/ │ └── grains_48_res_128_samples_8/ ├── results/ 3. Directory Details datasets/ Contains processed material parameter and stress tensor data in .npy format for use in neural operator training and evaluation. Each dataset folder includes: Material parameters: E.npy (Young's modulus), v.npy (Poisson's ratio) Stress tensor components: P11.npy, P22.npy, P23.npy,P32.npy,P33.npy (stress tensor) Metadata: input_param_data.json.npy results/ Contains output quantities. The trained models: *PeFNO.eqx *PgFNO.eqx *PiFNO.eqx The loss histories and losses on the test set: *best_model_losses.npy *test_losses.json The results of the experiments: *hyper_param.json *resultsGridSearch.pkl *resultsSensAnaCoefLoss.pkl