{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/variational-autoencoding-the-lagrangian","title":"Variational Autoencoding the Lagrangian Trajectories of Particles in a Combustion System","arxiv_id":"1811.11896","date":"2018-11-29","proceeding":null,"authors":["Pai Liu","Jingwei Gan","Rajan K. Chakrabarty"],"abstract":"We introduce a deep learning method to simulate the motion of particles\ntrapped in a chaotic recirculating flame. The Lagrangian trajectories of\nparticles, captured using a high-speed camera and subsequently reconstructed in\n3-dimensional space, were used to train a variational autoencoder (VAE) which\ncomprises multiple layers of convolutional neural networks. We show that the\ntrajectories, which are statistically representative of those determined in\nexperiments, can be generated using the VAE network. The performance of our\nmodel is evaluated with respect to the accuracy and generalization of the\noutputs.","url_abs":"http://arxiv.org/abs/1811.11896v2","url_pdf":"http://arxiv.org/pdf/1811.11896v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"variational-autoencoding-the-lagrangian","repo_url":"https://github.com/deadzombie2333/Lagrangian_simulation_VAE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}