Emulating the interstellar medium chemistry with neural operators
Lorenzo Branca Andrea Pallottini
Galaxy formation and evolution critically depend on understanding the complex
photo-chemical processes that govern the evolution and thermodynamics of the
InterStellar Medium (ISM). Computationally, solving chemistry is among the most
heavy tasks in cosmological and astrophysical simulations. The evolution of
such non-equilibrium photo-chemical network relies on implicit, precise,
computationally costly, ordinary differential equations (ODE) solvers. Here, we
aim at substituting such procedural solvers with fast, pre-trained, emulators
based on neural operators. We emulate a non-equilibrium chemical network up to
H$_2$ formation (9 species, 52 reactions) by adopting the DeepONet formalism,
i.e. by splitting the ODE solver operator that maps the initial conditions and
time evolution into a tensor product of two neural networks. We use
$\texttt{KROME}$ to generate a training set spanning $-2\leq
\log(n/\mathrm{cm}^{-3}) \leq 3.5$, $\log(20) \leq\log(T/\mathrm{K}) \leq 5.5$,
$-6 \leq \log(n_i/n) < 0$, and by adopting an incident radiation field
$\textbf{F}$ sampled in 10 energy bins with a continuity prior. We separately
train the solver for $T$ and each $n_i$ for $\simeq 4.34\,\rm GPUhrs$. Compared
with the reference solutions obtained by $\texttt{KROME}$ for single zone
models, the typical precision obtained is of order $10^{-2}$, i.e. the $10
\times$ better with a training that is $40 \times$ less costly with respect to
previous emulators which however considered only a fixed $\mathbf{F}$. The
present model achieves a speed-up of a factor of $128 \times$ with respect to
stiff ODE solvers. Our neural emulator represents a significant leap forward in
the modeling of ISM chemistry, offering a good balance of precision,
versatility, and computational efficiency.