The field of nonlinear system modeling and state estimation is witnessing significant developments, with a focus on improving accuracy, robustness, and interpretability. Researchers are exploring innovative approaches to integrate system dynamics and sensor data into physics-informed learning processes, enabling more accurate state estimation and better handling of noisy measurements. Noteworthy papers include PINN-Obs, which introduces a novel Adaptive Physics-Informed Neural Network-based Observer for accurate state estimation in nonlinear systems. Noisy PDE Training Requires Bigger PINNs proves a lower bound on the size of neural networks required for effective PINN training with noisy data. These advances have the potential to enhance estimation accuracy and robustness in various applications, including control systems and state-space modeling.