Operator learning meets inverse problems: A probabilistic perspective
The paper explores using operator learning to solve inverse problems, which are difficult mapping tasks. It investigates two main strategies: creating end-to-end solvers that map data directly to solutions and learning modular components like priors and regularizers within traditional algorithms. This probabilistic approach offers a framework for handling complex, infinite-dimensional function spaces.