Reinforcement learning optimisation for graded metamaterial design using a physical-based constraint on the state representation and action space.

ABSTRACT: The energy harvesting capability of a graded metamaterial is maximised via reinforcement learning (RL) under realistic excitations at the microscale. The metamaterial consists of a waveguide with a set of beam-like resonators of variable length, with piezoelectric patches, attached to it. The piezo-mechanical system is modelled through equivalent lumped parameters determined via a general impedance analysis. Realistic conditions are mimicked by considering either magnetic loading or random excitations, the latter scenario requiring the enhancement of the harvesting capability for a class of forcing terms with similar but different frequency content. The RL-based optimisation is empowered by using the physical understanding of wave propagation in a such local resonance system to constrain t

SUBMITTER: Rosafalco L 

PROVIDER: S-EPMC10710405 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature