The use of artificial neural networks (ANNs) in surrogate modeling, driven by advances in machine learning and deep learning, has therefore become a field of growing interest. While neural networks-based material modeling can be traced back to , it is in the last decade, with the rapid progress in deep learning and the availability of powerful hardware, that the development of surrogate models using ANNs has surged and continues to expand. Data utilized for constructing these surrogate models can comprise a combination of experimental data, empirical knowledge, and synthetic data generated through numerical solvers. Within the realm of continuum mechanics, numerous methodologies have emerged for building surrogate models using ANNs. For example, in a neural network architecture, namely, conditional generative adversarial networks, has been employed to predict stress and stress fields for a given microstructure geometry; employed a convolutional neural network (CNN) to estimate von Mises stress for microstructures consisting of isotropic elastic and elastoplastic grains, within microstructures, with extensions to heterogeneous periodic microstructures containing inelastic crystal grains in as depicted in Fig. . Furthermore, explores the application of the Fourier Neural Operator (FNO) for the surrogate modeling of stress and strain in heterogeneous composites. The figure is modified from and with permission.