Radiative Transfer

Radiative transfer modeling describes how electromagnetic radiation interacts with matter, a fundamental process across diverse scientific domains. Current research emphasizes improving the accuracy and efficiency of radiative transfer calculations, particularly through the application of machine learning techniques like physics-informed neural networks (PINNs), Fourier neural operators (FNOs), and deep learning autoencoders, often used to emulate computationally expensive traditional models. These advancements are crucial for enhancing the precision of climate modeling, exoplanet characterization, remote sensing applications (e.g., monitoring marine debris or forest biophysical properties), and high-energy density physics simulations, ultimately leading to more accurate predictions and improved scientific understanding.