Efficient Neural Approximation of PDE Backstepping Controller Gains

This work proposes a methodology to directly approximate backstepping gains using neural operators, bypassing the need to approximate the full backstepping kernels. This approach simplifies the operator being approximated and the training of its neural approximation, with an expected reduction in the neural network size.