Summary
Machine learning and artificial intelligence has been successful in the approximation and prediction of complex physical phenomena. A key aspect is the development of models capable of capturing dependencies on input parameters, domain configurations, boundary conditions, initial states, and spacetime coordinates within one neural network. One approach is operator learning, which encodes the solution operators of parametric partial differential equations into neural networks. However, the size of these neural networks often grows with the complexity of the task, that is, the accuracy of the me
