Speeding up astrochemical reaction networks with autoencoders and neural
  ODEs
Immanuel Sulzer Tobias Buck
In astrophysics, solving complex chemical reaction networks is essential but
computationally demanding due to the high dimensionality and stiffness of the
ODE systems. Traditional approaches for reducing computational load are often
specialized to specific chemical networks and require expert knowledge. This
paper introduces a machine learning-based solution employing autoencoders for
dimensionality reduction and a latent space neural ODE solver to accelerate
astrochemical reaction network computations. Additionally, we propose a
cost-effective latent space linear function solver as an alternative to neural
ODEs. These methods are assessed on a dataset comprising 29 chemical species
and 224 reactions. Our findings demonstrate that the neural ODE achieves a 55x
speedup over the baseline model while maintaining significantly higher accuracy
by up to two orders of magnitude reduction in relative error. Furthermore, the
linear latent model enhances accuracy and achieves a speedup of up to 4000x
compared to standard methods.