Researchers are exploring various frameworks and methods for improving neural network solvers and operator learning, particularly in the context of partial differential equations and physics-informed modeling. Different approaches, including residual-based adaptivity, finite element neural networks, and convolutional neural networks, are being investigated for tasks such as simulation, parameter identification, and uncertainty quantification. The development of these frameworks and methods aims to enhance the efficiency and accuracy of neural network-based solutions for complex problems.