## 1 Introduction  
In tissue engineering, biocompatible materials (i.e., biomaterials) are employed to generate hydrogels or other scaffolds for use in the repair or replacement of damaged and diseased tissues. To reduce the risk of scar tissue formation at the interface with the host tissues, biomimetic materials that match the physicochemical properties of native tissue are commonly desired. Collagen (COL) is the most abundant protein in mammals [1], which could be reinforced by bioglass (BG) to formulate composite scaffolds with improved mechanical properties for bone tissue engineering applications [2]. Specifically, [PERSON] et al. [3] showed that incorporating BG into COL reduced the swelling and improved the stability and rheological properties (i.e., yield stress) of COL hydrogels. [PERSON] et al. [4] reported, in a review paper, that the stiffness of COL scaffolds significantly increased upon adding BGs. The mechanical properties of BG-COL composite scaffolds have been estimated from either a mathematical model or finite element (FE) method [5]. Homogenization methods, such as the double inclusion method, Mori-Tanaka mean field method, and self-consistent approaches, are generally practical for simple microstructures [6; 7; 8; 9]. The representative volume element (RVE) technique in the FE method has been well utilized to estimate the effective properties of composite materials [10; 11; 12]. Prior work has shown that the mechanical properties of BG-COL scaffolds depend on the concentration, spatial distribution, and particle size of the BG, as well as the fabrication process [13]. Machine learning methods, especially deep learning methods, have been well utilized in solving engineering problems, e.g., predicting atomic and molecular properties, crystal structures and stability, stent expansion, and retinal mechanics [14; 15; 16; 17]. Specifically, deep neural network (DNN) approaches have proven efficient in uncovering unique structures. With sufficient training data, DNN uses the high-dimensional feature vector from the original data and learns the feature vector's nonlinear relationship with the expected output [18; 19]. [PERSON] et al. [20; 21] demonstrated that DNN could efficiently provide an accurate mapping between the effective mechanical properties (Young's modulus and Poisson's ratio) and the microstructures of composites. A supervised ML was presented by [PERSON] et al. [22] to predict the homogenized elastic properties of two-phase materials. [PERSON] et al. [23] predicted the stress-strain curve of binary composites by using a combination of principal component analysis and convolutional neural networks (CNN). [PERSON] et al. [24] presented a FE-based evolutionary algorithm to design active composite structures for 4D printing that can achieve target shape shifting responses. [PERSON] et al. [25] introduced a deep learning approach for predicting unidirectional fiber-reinforced composites' transverse elastic and plastic properties. [PERSON] et al. [26] utilized different machine learning methods, including CNN, support vector regression (SVR), and Gaussian process regression (GPR), to predict the effective thermal conductivity of composite materials. [PERSON] et al. [27] employed 2D CNN to predict the effective thermal conductivity of 3D composites using 2D cross-section images. [PERSON] et al. [28] predicted the transverse mechanical behavior of composites in terms of the stress-strain curves by implementing CNN. They considered the volume fraction (_V/f_) percentage of the particles as 40%, 50%, and 60%. However, the ML models have not been applied for predicting the mechanical properties of composite hydrogels. The current study is the first attempt to adopt an FE-based ML approach for predicting the mechanical properties of BG-COL. The integration of FE and ML methods, a.k.a. FE-based ML approach, is still in its early stage.