Accelerating Radiative Transfer in Planetary Atmospheres by Orders of Magnitude with Neural Network Emulators

Radiative transfer calculations are essential for modeling planetary atmospheres. However, standard methods are computationally demanding and impose accuracy-speed trade-offs. High computational costs force numerical simplifications in large models (e.g., general circulation models) that degrade the accuracy of the simulation. Radiative transfer calculations are an ideal candidate for machine learning emulation; fundamentally, radiative transfer is a well-defined physical mapping from a static atmospheric profile to the resulting fluxes, and high-fidelity training data can be created from first-principles calculations. We developed a radiative transfer emulator using an encoder-only transformer neural network architecture, trained on 1D profiles representative of solar-composition hot Jupiter atmospheres. Our emulator reproduced bolometric two-stream layer fluxes with mean test set errors of ∼1% compared to the traditional method and achieved speedups of more than 100× in batched GPU inference. In matched comparisons against a long short-term memory model (LSTM) baseline, a transformer was consistently the more accurate and more robust architecture when training data were limited, while the LSTM achieved better accuracy at full scale. Emulating radiative transfer with machine learning opens up the possibility for faster and more accurate routines within planetary atmospheric models.