## 1 Introduction  
Atherosclerosis is a complex cardiovascular disease characterized by plaque accumulation in the arterial walls, leading to the narrowing and hardening of the arteries. It is a major cause of heart disease, stroke, and other cardiovascular diseases, making it one of the leading causes of death worldwide. The disease is caused by genetic, environmental, and lifestyle factors, including high cholesterol, high blood pressure, smoking, obesity, and diabetes [1, 2, 3]. To effectively combat this prevalent disease, it is imperative to develop a deep understanding of its underlying mechanics, particularly in plaque development and rupture. This study centers on unveiling the intricacies of stress and strain distribution within arterial walls, a crucial aspect in predicting and preventing plaque rupture. The mechanical properties of the plaque, such as its stiffness and strength, are important determinants of its stability and the risk of rupture. Therefore, predicting the stress-strain field maps of the plaque can provide valuable insights into its mechanical behavior and help identify regions that may be prone to rupture. Several studies have demonstrated the potential of computational models and imaging techniques for predicting the stress-strain field maps of atherosclerotic plaques and assessing their risk of rupture [4; 5]. [PERSON] et al. [6] found that maximum von Mises stress was significantly higher in ruptured plaques compared to stable plaques. Therefore, predicting the von Mises stress could be an essential tool for preventing plaque rupture and reducing the incidence of cardiovascular events. However, there exists limited consideration of variability in arterial wall geometries and plaque characteristics. It is essential to characterize how spatial variations in calcification affect stress and strain distribution and present a novel angle in the biomechanical analysis of atherosclerosis. The FEM, known as the finite element method, is the conventional numerical technique employed for stress-strain analysis of structures. It revolves around solving partial differential equations to evaluate the system's behavior [7; 8; 9]. Despite the widespread use of FEM in simulating complex biomechanical phenomena, these simulations can become computationally expensive and time-consuming, particularly when dealing with highly nonlinear analyses or intricate geometries common in the study of atherosclerosis. There has been a significant shift towards employing ML techniques to mitigate these challenges, renowned for their computational speed efficiency and adeptness at navigating complex spatial arrangements. These ML techniques serve as potent surrogate models, aptly demonstrated in existing literature [10; 11; 12; 13; 14; 15; 16; 17; 18; 19], providing a promising alternative to FEM for predicting relevant quantities of interest in biomechanical studies. The integration of ML in biomechanical research on atherosclerosis signifies a transformative step towards more rapid and efficient analyses, fostering a quicker translation of research findings to clinical applications. The transition ensures the maintenance of computational accuracy and significantly reduces the computational resources and time required, presenting a more streamlined and efficient approach to biomechanical analysis. [PERSON] et al. [20] presented a method for bridging FEM and ML for predicting the von Mises stress distribution in arterial walls affected by atherosclerosis. The authors utilized a FEM to simulate the mechanical behavior of arterial walls and generated a large dataset of stress values for varying degrees of plaque build-up. The dataset was then used to train an ML model that could predict stress values based on input parameters such as plaque thickness and the diameter of the arterial lumen. [PERSON] et al. [21] used a CNN model to automatically segment atherosclerotic plaques in intravascular ultrasound (IVUS) images. The authors used a dataset of IVUS images and achieved an accuracy of 90% in plaque segmentation.