Paper on Machine Learning in Scientific Reports

Christian's work on machine learning for the simultaneous damping of many mechanical modes has been published in Scientific Reports!

We apply adaptive feedback for the partial refrigeration of a mechanical resonator, i.e. with the aim to simultaneously cool the classical thermal motion of more than one vibrational degree of freedom. The feedback is obtained from a neural network parametrized policy trained via a reinforcement learning strategy to choose the correct sequence of actions from a fnite set in order to simultaneously reduce the energy of many modes of vibration. The actions are realized either as optical modulations of the spring constants in the so-called quadratic optomechanical coupling regime or as radiation pressure induced momentum kicks in the linear coupling regime. As a proof of principle we numerically illustrate efcient simultaneous cooling of four independent modes with an overall strong reduction of the total system temperature.

Click here for the link to the journal.