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Modelling the complex physics of the Interstellar Medium (ISM) in the contextof large-scale numerical simulations is a challenging task. A number of methodshave been proposed to embed a description of the ISM into different codes. Wepropose a new way to achieve this task: Artificial Neural Networks (ANNs). TheANN has been trained on a pre-compiled model database, and its predictions havebeen compared to the expected theoretical ones, finding good agreement both instatic and in dynamical tests run using the Padova Tree-SPH code \textsc{EvoL}.A neural network can reproduce the details of the interstellar gas evolution,requiring limited computational resources. We suggest that such an algorithmcan replace a real-time calculation of mass elements chemical evolution inhydrodynamical codes.

MaNN: Multiple Artificial Neural Networks for modelling the Interstellar Medium