  We demonstrate a multi-fidelity (MF) machine learning ensemble framework for
the inverse design of photonic surfaces, trained on a dataset of 11,759 samples
that we fabricate using high throughput femtosecond laser processing. The MF
ensemble combines an initial low fidelity model for generating design
solutions, with a high fidelity model that refines these solutions through
local optimization. The combined MF ensemble can generate multiple disparate
sets of laser-processing parameters that can each produce the same target input
spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley
Additive exPlanations analysis shows transparent model interpretability of the
complex relationship between laser parameters and spectral emissivity. Finally,
the MF ensemble is experimentally validated by fabricating and evaluating
photonic surface designs that it generates for improved efficiency energy
harvesting devices. Our approach provides a powerful tool for advancing the
inverse design of photonic surfaces in energy harvesting applications.
