Reinforcement learning accelerates model-free training of optical AI systems
Optical computing has emerged as a powerful approach for high-speed and energy-efficient information processing. Diffractive optical networks, in particular, enable large-scale parallel computation through the use of passive structured phase masks and the propagation of light. However, one major challenge remains: systems trained in model-based simulations often fail to perform optimally in real experimental settings, where misalignments, noise, and model inaccuracies are difficult to capture.
Optical tactile sensors are gaining significant attention as next-generation biometric recognition technologies. Capable of analyzing dynamic forces from a single image, these sensors transcend the limitations of existing optical systems, creating potential applications in diverse fields, such as handwriting emotion analysis, surface characterization, and anti-counterfeiting measures.
Partial differential equations (PDEs) are a class of mathematical problems that represent the interplay of multiple variables, and therefore have predictive power when it comes to complex physical systems. Solving these equations is a perpetual challenge, however, and current computational techniques for doing so are time-consuming and expensive.
One of the promising technologies being developed for next-generation augmented/virtual reality (AR/VR) systems is holographic image displays that use coherent light illumination to emulate the 3D optical waves representing, for example, the objects within a scene. These holographic image displays can potentially simplify the optical setup of a wearable display,…