## 1 Introduction
The segmentation results were then used to simulate the mechanical properties of the plaque using FEM. The study showed that the mechanical properties of the plaque were highly dependent on its morphology and composition. [PERSON] et al. [22] performed Optical Coherence Tomography (OCT) imaging of atherosclerotic plaques in human cadaveric coronary arteries and used finite element analysis to assess the mechanical properties of the plaques. They found that the fibrous cap's thickness and the lipid core's size were important factors in determining the mechanical stability of the plaque. Plaques with thinner fibrous caps and larger lipid cores were found to have higher stress concentrations, which may contribute to plaque rupture. [PERSON] et al. [23] presented a study on applying ML techniques to determine plaque vulnerability, an important factor in predicting the risk of heart attack and stroke. The authors discuss the limitations of traditional methods for assessing plaque vulnerability and the potential benefits of using ML techniques to analyze patient imaging data. The study involved analyzing patients with carotid artery disease, and the results show that ML techniques can effectively determine plaque vulnerability. The authors used conventional ML algorithms, such as support vector machines (SVM) and random forests (RF), to analyze imaging data and extract relevant features. They also developed a new \"plaque volume score\" method combining different features to predict plaque vulnerability better. The results show that ML algorithms can accurately predict plaque vulnerability, with an accuracy of up to 90%. The authors suggest that these techniques can be used in clinical practice to improve risk assessment and treatment planning for patients with carotid artery disease. While all these papers discuss predicting stress distribution, none of them address the issue of how well their predictions would hold up in the face of variability in arterial wall geometries, such as changes in calcification angle, thickness, and number, or shifts in lumen location and fibrous thickness. Our innovative approach considers the variability in calcification numbers while predicting stress and strain in arterial walls. This study aims to employ deep learning models for predicting stress and strain distribution in arterial walls under blood pressure loading, specifically considering various spatial calcification distributions. Encoder-decoder networks, particularly U-Net networks [24], have demonstrated effective image mapping due to their ability to capture high-resolution details and low-level features by propagating context information from lower to higher layers. Moreover, skip connections help prevent the vanishing gradient problem during model training. Conversely, cGAN, which learns a mapping from an observed image and random noise vector to an output image, is utilized for predicting stress-strain maps using a U-Net generator architecture and a PatchGAN discriminator, highlighting the superiority of cGAN in predicting strain maps of arterial walls compared to the U-Net model, particularly from the perceptual perspective [25]. This research implements deep learning models incorporating U-Net and cGAN, showcasing their generalization ability in predicting stress and strain maps of arterial walls. Moreover, we demonstrate the benefits of using ensemble and transfer learning strategies for improved accuracy. Impressively, our results display high accuracy even when trained on a mere 4000 images, which we achieve through data augmentation grounded in the underlying physics of the issue. The structure of the manuscript is as follows: In Section 2, the proposed methodology is discussed. Section 3 showcases the prediction results and analysis, while Section 4 offers discussions and conclusions.