Data Augmentation for learning mechanical digital twins of voids in welding joints

In Source-2_Data_Augmentation: Exercice1_augmentation.ipynb Jupyter Notebook for data warpping of defect images. Exercice2_augmentation_multimodale.ipynb Jupyter Notebook for multimodal data augmentaion (defect images and mechanical fields) via oversampling Exercice3_clustering.ipynb Data clustering using the k-medoids algorithm applied to mechanical dissimilarity of the defects. k_medoids.py is a python code of a kmedoids algorithm. in Data: All_images.npy (numpy file) contains the defect images. All_Stresses.npy (numpy) contains mechanical fields, All_Stresses[k,i,j,ic,it] is the instance number k of the component ic of the Cauchy stress tensor at time it. The mechanical problem is decribed in ⟨10.5802/crmeca.51⟩. ⟨hal-03113503⟩. New_images_1.npy and New_Stresses_1.npy are augmented data for k=1. New_images_87.npy and New_Stresses_87.npy are augmented data for k=87. Dissimilarity_Stress.npy is the Frobenius norm of the distances between stress tensors (All_Stresses.npy).