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).