By combining sensitivity analysis for dimension reduction with multi-fidelity optimization, you can reduce expensive simulator calls by 50%+ while maintaining optimization quality—critical for industrial design where each simulation costs hours or days.
This paper presents a method to optimize complex industrial process simulations more efficiently by combining dimensionality reduction with multi-fidelity Bayesian optimization. The approach uses cheap approximations alongside expensive detailed simulations, intelligently deciding when to use each, reducing the total computational cost while maintaining solution quality.