  This research introduces an innovative application of physics-informed neural
networks (PINNs) to tackle the intricate challenges of radiative transfer (RT)
modeling in exoplanetary atmospheres, with a special focus on efficiently
handling scattering phenomena. Traditional RT models often simplify scattering
as absorption, leading to inaccuracies. Our approach utilizes PINNs, noted for
their ability to incorporate the governing differential equations of RT
directly into their loss function, thus offering a more precise yet potentially
fast modeling technique. The core of our method involves the development of a
parameterized PINN tailored for a modified RT equation, enhancing its
adaptability to various atmospheric scenarios. We focus on RT in transiting
exoplanet atmospheres using a simplified 1D isothermal model with
pressure-dependent coefficients for absorption and Rayleigh scattering. In
scenarios of pure absorption, the PINN demonstrates its effectiveness in
predicting transmission spectra for diverse absorption profiles. For Rayleigh
scattering, the network successfully computes the RT equation, addressing both
direct and diffuse stellar light components. While our preliminary results with
simplified models are promising, indicating the potential of PINNs in improving
RT calculations, we acknowledge the errors stemming from our approximations as
well as the challenges in applying this technique to more complex atmospheric
conditions. Specifically, extending our approach to atmospheres with intricate
temperature-pressure profiles and varying scattering properties, such as those
introduced by clouds and hazes, remains a significant area for future
development.
