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Journal Article

Reducing the complexity of chemical networks via interpretable autoencoders


Grassi,  T.
Center for Astrochemical Studies at MPE, MPI for Extraterrestrial Physics, Max Planck Society;

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Grassi, T., Nauman, F., Ramsey, J. P., Bovino, S., Picogna, G., & Ercolano, B. (2022). Reducing the complexity of chemical networks via interpretable autoencoders. Astronomy and Astrophysics, 668: A139. doi:10.1051/0004-6361/202039956.

Cite as: https://hdl.handle.net/21.11116/0000-000C-D0DC-1
In many astrophysical applications, the cost of solving a chemical network represented by a system of ordinary differential equations (ODEs) grows significantly with the size of the network and can often represent a significant computational bottleneck, particularly in coupled chemo-dynamical models. Although standard numerical techniques and complex solutions tailored to thermochemistry can somewhat reduce the cost, more recently, machine learning algorithms have begun to attack this challenge via data-driven dimensional reduction techniques. In this work, we present a new class of methods that take advantage of machine learning techniques to reduce complex data sets (autoencoders), the optimization of multiparameter systems (standard backpropagation), and the robustness of well-established ODE solvers to to explicitly incorporate time dependence. This new method allows us to find a compressed and simplified version of a large chemical network in a semiautomated fashion that can be solved with a standard ODE solver, while also enabling interpretability of the compressed, latent network. As a proof of concept, we tested the method on an astrophysically relevant chemical network with 29 species and 224 reactions, obtaining a reduced but representative network with only 5 species and 12 reactions, and an increase in speed by a factor 65.