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Abstract In this paper, we consider the optimal control of material microstructures. Such material microstructures are modeled by the so-called phase-field model. We study the underlying physical structure of the model and propose a data-based approach for its optimal control, along with a comparison to the control using a state-of-the-art reinforcement learning (RL) algorithm. Simulation results show the feasibility of optimally controlling such microstructures to attain desired material properties and complex target microstructures.
Sharma et al. (Mon,) studied this question.