## 4 Discussion  
In summary, this research aims to explore the potential of deep learning tools in predicting stress-strain fields within 2D cross sections of arterial wall geometries, which can replace the traditional FEM method. To establish a relationship between the spatial distribution of calcification in arterial wall cross-sections and the von Mises stress and strain fields, two ML networks were investigated. The first network utilized a U-Net architecture within a CNN model, while the second employed a cGAN. The trained models can accurately predict stress-strain field maps for arterial walls with varying quantities and spatial configurations of calcification, which is a significant achievement. To generate the training dataset, a Python code was utilized to create various shapes of arterial walls with randomized features such as location, angle, thickness, number of calcifications, lumen area location, and diameters, drawing from the simplified clinical data and our observations. The dataset consists of input and output images obtained by applying boundary conditions and extracting stress-strain field maps. The trained U-Net models produce precise predictions of the von Mises stress and strain fields, with SSIM of 0.854 and 0.830 and MSE of 0.017 and 0.018 for stress and strain, respectively, on a held-out test set. Additionally, the stress and strain data-trained cGAN model outperforms the CNN model in prediction accuracy, demonstrating SSIM of 0.890 and 0.803 and MSE of 0.008 and 0.017 for stress and strain, respectively. However, the utilized image quality metrics have limitations. They cannot encompass all the image perception details significant for human visual systems. We expect the performance of cGAN to be evaluated more accurately and fairly if a comprehensive metric could be designed in the future. This research demonstrates the ability of a surrogate model for finite element analysis to accurately predict stress-strain fields of arterial walls, utilizing 2D cross-sectional images. Furthermore, we showcased the numerous benefits of utilizing ensemble and transfer learning methodologies to achieve superior levels of precision. By combining multiple models, an ensemble approach can significantly enhance predictive performance, as the diverse models can compensate for each other's weaknesses and amplify their strengths. On the other hand, transfer learning can leverage the knowledge gained from a pre-trained model and apply it to a new task with a limited amount of data. This approach can drastically reduce the required training and improve the model's performance on the new task. Our findings indicate that by ensembling the same U-Net with various hyperparameters, the mean SSIM across all testing data can be boosted from 0.814 to 0.825. Moreover, by incorporating an additional layer into the standard U-Net and training two models thrice, we achieved the highest SSIM of 0.830 for predicting the strain map. Transfer learning was utilized in addition to the [PERSON] stress-trained model to forecast strain maps. By comparing the loss of a U-Net model trained from scratch to that of a pretrained U-Net model trained with von Mises stress data over 100 epochs, it was noted that both models displayed convergence and exhibited similar behavior during training. Despite this, the transfer learning model demonstrated quicker convergence and produced an average SSIM of 0.820 for all test data, outperforming a U-Net trained from scratch. We employed a cGAN for stress and strain map predictions and compared the outcomeswith those derived from a U-Net. The optimal stress map predictions were attained by assembling two generator architectures in the cGAN, achieving an SSIM of 0.890 (Table 3). Despite this, U-Net demonstrated superior performance in strain prediction, obtaining the highest SSIM of 0.830 when combined with two U-Net architectures.