Neural operators can compress high-dimensional design spaces into low-dimensional latent representations that preserve physics-aware structure, making evolutionary optimization practical for inverse design problems that would otherwise be intractable.
This paper combines neural operators with evolutionary optimization to solve inverse design problems for physical systems governed by PDEs. By learning a compact representation of design space topology and coupling it with CMA-ES, the method reduces design dimensionality dramatically while maintaining high performance across different operating conditions.