A Physics-Informed Neural Network as a Digital Twin of Optically Turbid Media

In this article, a novel methodology for characterizing and creating a digital twin of turbid media with intensity measurements only is presented, employing a unique physics-informed neural network approach. Unlike previous approaches utilizing various deep neural network architectures that often function as black boxes, the method prioritizes interpretability, offering a clearer understanding of the underlying processes of light propagation through turbid media and estimating the transmission matrix. The possibility and use case of gradient calculation through this presented digital twin are showcased by solving the problem to retrieve the initial wavefront shape of the light that passed through the medium (e.g., the image transmission problem). The results surpassed the accuracy of models directly optimized for this task, underscoring the precision of the proposed digital twin. This capability represents a pivotal advancement for future developments in neuromorphic and deep learning computation and training using such a photonic and optical systems.