Using Graph Neural Networks to Learn Fire and Smoke Behaviour in a Physics- Based Simulator

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Examensarbete för masterexamen
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2021
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Lam, Edvin
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Deep learning is prominent in computer vision as a means of learning spatial features and processing them in useful ways, such as for image classification and image segmentation. An application of this in physics is to use videos of fire to predict how the flames and smoke evolve over time. This thesis explores a simplified version of this in an attempt to lay the groundwork for future studies. In particular, simulations of fire and smoke were made using the open source software Fire Dynamics Simulator by NIST. From these simulations, frame by frame states of the physical system could be extracted. A graph neural network using the MeshGraphNets framework was trained to predict future states of the fire given two past states as input. Some of the resulting neural networks were able to reasonably predict over 50 frames into the future and also showed some competence when faced with test sets with a different physical geometry. The performances of the trained models varied greatly depending on the loss functions used in training, suggesting that more hyperparameter optimisation could be done in this regard.
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graph neural network, fire dynamics simulator, deep learning, physics simulation
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