Adrien Weihs

Kernel Methods for Learning Operators with Multiple Inputs and Outputs

This work introduces a general kernel-based encoder-decoder framework for operator learning that separates observation, representation, learning, and reconstruction, and develops this framework for multi-input, multi-output operator learning, where operators map between products of potentially distinct function spaces.