Light transport in ocular tissues is critical for optimizing laser therapies in medical physics. The complex structure of tissues complicates accurate modeling. The Radiative Transfer Equation (RTE) describes light propagation, accounting for scattering and absorption. Solving RTE numerically is challenging, but Physics-Informed Neural Networks (PINNs) offer a promising solution by integrating physical laws with machine learning. This study employs PINNs to model light transport in the cornea, enhancing accuracy and efficiency.
