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 andto measure the delayed feedback employed, using the trapped particle trajectory asan input. We demonstrate that this approach is outperforming variance methods inorder to calibrate stiffness, also outperforming approximation method to measure the delay in harmonic trap case.
Handledare: Aykut Argun
Examinator: Giovanni Volpe
Opponent: Ivan Gentile Japiassu
Nexus 4030, meeting room, Kemigården 1, Fysik Origo