Speaker
Description
The design optimization of RFQ and DTL cavities involve expensive black-box simulations, difficult constraints coming from simulation failures and the absence of reliable derivative information. These characteristics make derivative-free optimization methods particularly interesting, yet their practical performance in a conjoint RFQ-DTL cavity design optimization remains insufficiently studied. This contribution presents a prototype optimization framework connecting the execution of established accelerator codes, automating post-processing and derivative-free optimization methods. This work discusses the performance of derivative-free methods such as MADS, BOBYQA and COBYLA comparing them to commonly used Bayesian approaches in black-box optimization. The framework under development also studies an inverse-design formulation: instead of only evaluating a proposed design, the optimizer searches for feasible input configurations that reproduce a selected target output while still trying to preserve the optimality of the point.
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