Application of Bayesian optimization for controlling particle behavior

Abstract

This paper presents a novel approach to optimizing the design of structures for controlling particle behavior through the application of Bayesian optimization techniques. Particle behavior is highly sensitive to initial conditions, leading to uncertainty in predicting behavior. To mitigate this predictive uncertainty, this research employs a probabilistic approach, namely Bayesian optimization. By defining the probability of particles achieving desired objectives as the objective function, Bayesian optimization facilitates probabilistic estimation for optimization. The results demonstrate that the present methodology effectively increases the probability of particles achieving objectives by optimizing the design variables of the structure. This study confirms the efficiency of Bayesian optimization in optimizing structures for controlling particle behavior. The methodology introduced in this paper offers a new solution for effectively addressing the sensitivity of initial conditions and uncertainty in particle behavior in the design optimization of particle-based systems.