Yanuar Rizki Pahlevi defended his Master thesis in MPCAS at the Chalmers University of Technology on 9 June 2022 at 17:00. Congrats!
Title: Deep Learning for Optical Tweezers. DeepCalib Implementation for Brownian Motion with Delayed Feedback
Brownian motion with delayed feedback theoretically studied to take control of Brownian particle movement’s direction. One can use optical tweezers to implement delayed feedback. Calibrating optical tweezers with delay implemented is not an easy job. In this study, Deep learning technique using Long Short Term Memory (LSTM) layer as main composition of the model to calibrate the trap stiffness and to measure the delayed feedback employed, using the trapped particle trajectory as an input. We demonstrate that this approach is outperforming variance methods in order to calibrate stiffness, also outperforming approximation method to measure the delay in harmonic trap case.
Name of the master programme: MPCAS – Complex Adaptive Systems
Examiner: Giovanni Volpe
Supervisor: Aykut Argun
Opponent: Ivan Gentile Japiassu
Place: Nexus
Time: 9 June, 2022, 17:00
