Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications

This paper introduces Human-in-the-Loop Meta Bayesian Optimization (HL-MBO), a framework combining expert knowledge with few-shot machine learning to accelerate discovery in data-scarce scientific domains. It outperforms current Bayesian Optimization methods in fusion energy yield optimization and other benchmarks.