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We introduce a deep learning method to simulate the motion of particlestrapped in a chaotic recirculating flame. The Lagrangian trajectories ofparticles, captured using a high-speed camera and subsequently reconstructed in3-dimensional space, were used to train a variational autoencoder (VAE) whichcomprises multiple layers of convolutional neural networks. We show that thetrajectories, which are statistically representative of those determined inexperiments, can be generated using the VAE network. The performance of ourmodel is evaluated with respect to the accuracy and generalization of theoutputs.

Variational Autoencoding the Lagrangian Trajectories of Particles in a Combustion System