Dynamic friction plays a key role in engineering and science, especially in earth-quake mechanics, where it impacts fault dynamics and energy distribution. Our goal is to better understand this complex process to improve predictions and refine earthquake mechanics based on existing developments to study the phenomenon of stick-slip. For this project, we use Physics-Informed Neural Networks (PINNs), a modern method that combines physical laws with neural networks to solve non-linear equations. The work is based on the study ”Physics-Informed Neural Networks: A Deep Learning Framework for Solving Forward and Inverse Problems Involving Non-linear Partial Differential Equations” by M. Raissi et al.