End-to-end physics-informed deep neural network optimization of sub-Nyquist lenses.

In this paper, an approach for optimizing sub-Nyquist lenses using an end-to-end physics-informed deep neural network is presented. The simulation and optimization of these sub-Nyquist lenses is investigated for image quality, classification performance, or both. This approach integrates a diffractive optical model with a deep learning classifier, forming a unified optimization framework that facilitates simultaneous simulation and optimization.

Measurement of the internal stress and electric field in a resonating piezoelectric transformer for high-voltage applications using the electro-optic and photoelastic effects.

The high output voltages from piezoelectric transformers are currently being used to accelerate charged particle beams for x-ray and neutron production. Traditional methods of characterizing piezoelectric transformers (PTs) using electrical probes can decrease the voltage transformation ratio of the device due to the introduction of load impedances on the order of hundreds of kiloohms to hundreds of megaohms. Consequently, an optical diagnostic was developed that used the photoelastic and electro-optic effects present in piezoelectric materials that are transparent to a given optical wavelength to determine the internal stress and electric field.