Deep Learning Applications in Photonics and Active Matter
Invited talk at the “Microscale Motion and Light” MPI-PKS Workshop, Dresden, Germany, 22-26 July 2019
After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in photonics and active matter. In particular, I will explain how we employed deep learning to enhance digital video microscopy , to estimate the properties of anomalous diffusion , and to improve the calculation of optical forces. Finally, I will provide an outlook for the application of deep learning in photonics and active matter.
 S. Helgadottir, A. Argun and G. Volpe, Digital video microscopy enhanced by deep learning. Optica 6(4), 506—513 (2019)
 S. Bo, F Schmidt, R. Eichborn and G. Volpe, Measurement of Anomalous Diffusion Using Recurrent Neural Networks. arXiv: 1905.02038