Gastone Pietro Rosati Papini

TL;DR: In this paper, a DEG system that is able to convert the oscillating energy carried by water waves into electricity is presented, which is a promising demonstration of the operation and effectiveness of ocean wave energy converters based on elastic capacitive generators.

TL;DR: A modelling approach which relies on the combination of nonlinear potential-flow hydrodynamics and electro-hyperelastic theory makes it possible to predict the system response in operational conditions, and thus it is employed to design and evaluate a DEG-based WEC that features an effective dynamic response.

TL;DR: Results show that pre-wiring effectively improves the performance and neural network models with a generic layer architecture and models with specialised topologies that hard-wire physics principles look to be preferable for several reasons.

TL;DR: In this paper, a model-based control strategy for a wave energy converter (WEC) based on dielectric elastomer generators (DEGs) is introduced.

TL;DR: A novel approach to learning predictive motor control via “mental simulations”, inspired by learning via mental imagery in natural Cognition, develops in two phases: first, the learning of predictive models based on data recorded in the interaction with the environment; then, at a deferred time, the synthesis of inverse models via offline episodic simulations.