  Photonic surfaces designed with specific optical characteristics are becoming
increasingly important for use in in various energy harvesting and storage
systems. , In this study, we develop a surrogate-based optimization approach
for designing such surfaces. The surrogate-based optimization framework employs
the Random Forest algorithm and uses a greedy, prediction-based exploration
strategy to identify the laser fabrication parameters that minimize the
discrepancy relative to a user-defined target optical characteristics. We
demonstrate the approach on two synthetic benchmarks and two specific cases of
photonic surface inverse design targets. It exhibits superior performance when
compared to other optimization algorithms across all benchmarks. Additionally,
we demonstrate a technique of inverse design warm starting for changed target
optical characteristics which enhances the performance of the introduced
approach.
