In many astrophysical applications, the cost of evolving in
time a chemical network represented by a system of ordinary differential
equations (ODEs) grows significantly with its size, and often causes a
critical computational bottleneck. I will introduce a new class of
methods that take advantage of machine learning techniques to reduce
complex data sets (autoencoders), the optimization of multi-parameter
systems (standard backpropagation), and the robustness of
well-established ODE solvers to explicitly incorporate time-dependence.
This new method allows us to find a compressed and simplified version of
a large chemical network in a semi-automated fashion that can be solved
with a standard ODE solver, while also enabling interpretability of the
compressed, latent network (https://arxiv.org/abs/2104.09516).
Giovanni Carraro
