Neural operator learning, which involves employing DNNs to learn mappings between infinite-dimensional function spaces, has recently gained significant attention, particularly for its applications in learning ODEs and PDEs. At the moment, there is a plethora of different neural operators, among which we can distinguish meta-architectures, e.g., deep operator networks (DeepONet) motivated by the universal approximation theorem for operators and operators based on integral transforms, e.g., the Fourier neural operator (FNO) , wavelet neural operator (WNO) , the graph kernel network (GKN) and the Laplace neural operator (LNO) , to name a few.