Prospects of reinforcement learning for the simultaneous damping of many mechanical modes

Sommer C, Asjad M, Genes C (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Scientific Reports Nature Publishing Group: Open Access Journals - Option B

Book Volume: 10

Article Number: 2623

Journal Issue: 1

DOI: 10.1038/s41598-020-59435-z

Abstract

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 finite 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 efficient simultaneous cooling of four independent modes with an overall strong reduction of the total system temperature.

Additional Organisation(s)

Department Physik

Involved external institutions

Max-Planck-Institut für die Physik des Lichts (MPL) / Max Planck Institute for the Science of Light DE Germany (DE)

How to cite

APA:

Sommer, C., Asjad, M., & Genes, C. (2020). Prospects of reinforcement learning for the simultaneous damping of many mechanical modes. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-59435-z

MLA:

Sommer, Christian, Muhammad Asjad, and Claudiu Genes. "Prospects of reinforcement learning for the simultaneous damping of many mechanical modes." Scientific Reports 10.1 (2020).

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