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.
As technology advances, and the demand for faster, higher-bandwidth, and more energy-efficient data processing continues to grow, scientists and engineers search for ways to improve electronic systems. One avenue they have been exploring is optoelectronics—the study and application of electronic devices that interface with light by detecting, emitting, or converting…