Refractive Index

Refractive index, a measure of how light bends when passing through a material, is crucial for diverse applications from atmospheric science to materials engineering. Current research focuses on improving the accuracy and efficiency of refractive index tomography, employing techniques like neural networks (including physics-informed neural networks and implicit neural representations) to reconstruct three-dimensional refractive index fields from limited data, often addressing challenges like the "missing cone" problem. These advancements are significantly impacting fields such as flow visualization, microscopy, and autonomous driving by enabling more precise and computationally efficient imaging and analysis of complex systems.