Speaker
Description
Understanding the interstellar medium (ISM) chemistry is pivotal for the study of galaxy formation and evolution. Traditional computational models rely on costly ordinary differential equation (ODE) solvers to simulate complex photo-chemical processes. This study introduces a novel approach using DeepONet neural operators to emulate a non-equilibrium chemical network, significantly reducing computational costs while maintaining precision. Unlike conventional methods, our approach approximates the differential operator directly, enabling the model to generalize beyond the specific conditions encountered during training. This capability ensures the robustness and flexibility of the emulation across a broader parameter space, including varied densities, temperatures, species abundances, and radiation fields. Remarkably, our method maintains an accuracy within 1\% while achieving speed-ups of up to 128× compared to traditional methods. This makes it a powerful tool for large-scale astrophysical simulations and advancing our understanding of ISM dynamics.
