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Simulation of physical systems with variants of the Interaction Network

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Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2020-2021. Tutor: Albert Díaz Guilera i Oriol Pujol Vila

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[en] The aim of this thesis is to primarily learn to predict trajectories of physical systems by using Machine Learning. We have used the Interaction Network as a base model and introduced tweaks to its structure in the framework of Graph Networks in order to deal with systems without interaction, with force-based interactions and systems governed by the Vicsek model. We have been able to replicate very well systems without interaction and systems governed by a Vicsek model of infinite reach. However the results with force-based systems are mediocre because they need more trainable parameters and training data. The results for the Vicsek model with a finite radius of reach are the worst but we have learned the necessity and the methodology
of introducing attention to the base model to deal with this class of problem.

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cc-by-nc-nd (c) Alfons Córdoba Meneses, 2021

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codi: Apache (c) Alfons Córdoba Meneses, 2021

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Màster Oficial - Fonaments de la Ciència de Dades

# metadata.dc.title[].value
Simulation of physical systems with variants of the Interaction Network