The field of physics-informed neural networks (PINNs) is rapidly advancing, with a focus on developing innovative methods for solving partial differential equations (PDEs) efficiently and accurately. Recent developments have led to the creation of hybrid frameworks that combine neural networks with traditional numerical methods, allowing for faster adaptation to new PDEs and improved generalization across different domains. Notable advancements include the integration of PDE residuals into pre-training, constraint-aware pre-training, and the use of epistemic PINNs to quantify uncertainty. These innovations have the potential to revolutionize the field of scientific computing and enable the solution of complex PDEs in a wide range of applications. Noteworthy papers in this area include: Physics-Informed Deep B-Spline Networks for Dynamical Systems, which proposes a hybrid framework for solving PDEs with varying parameters and initial conditions. Paving the way for scientific foundation models, which introduces a constraint-aware pre-training method for enhancing generalization and robustness in PDEs. E-PINNs: Epistemic Physics-Informed Neural Networks, which presents a framework for efficiently quantifying uncertainty in PINNs.