The field of material modeling is witnessing a significant shift towards the adoption of physics-informed neural networks (PINNs) and other innovative machine learning approaches. These methods are being used to improve the accuracy and efficiency of material modeling, particularly in areas such as anisotropic plasticity, multi-material elasticity, and fatigue life prediction. Researchers are exploring the use of PINNs to develop thermodynamically consistent constitutive models, which can accurately predict material behavior under various loading conditions. Additionally, the use of neural networks to learn surrogate PDE kernels is showing promise in predicting effective properties of composite materials. Noteworthy papers in this area include: The paper on Thermodynamically Consistent Hybrid and Permutation-Invariant Neural Yield Functions for Anisotropic Plasticity, which proposes a novel framework for modeling plastic anisotropy in metals. The paper on Physics-Informed Kolmogorov-Arnold Networks for multi-material elasticity problems, which introduces a new method for analyzing elasticity problems in electronic packaging multi-material structures. The paper on Neural Contrast Expansion for Explainable Structure-Property Prediction and Random Microstructure Design, which presents a cost-effective and explainable structure-property model for predicting effective properties of composite materials. The paper on Physics-informed neural network for fatigue life prediction of irradiated austenitic and ferritic/martensitic steels, which develops a PINN framework for predicting the low-cycle fatigue life of irradiated steels.