{"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-encoding-of-complex-dynamics","title":"Variational Encoding of Complex Dynamics","arxiv_id":"1711.08576","date":"2017-11-23","proceeding":null,"authors":["Carlos X. Hernández","Hannah K. Wayment-Steele","Mohammad M. Sultan","Brooke E. Husic","Vijay S. Pande"],"abstract":"Often the analysis of time-dependent chemical and biophysical systems\nproduces high-dimensional time-series data for which it can be difficult to\ninterpret which individual features are most salient. While recent work from\nour group and others has demonstrated the utility of time-lagged co-variate\nmodels to study such systems, linearity assumptions can limit the compression\nof inherently nonlinear dynamics into just a few characteristic components.\nRecent work in the field of deep learning has led to the development of\nvariational autoencoders (VAE), which are able to compress complex datasets\ninto simpler manifolds. We present the use of a time-lagged VAE, or variational\ndynamics encoder (VDE), to reduce complex, nonlinear processes to a single\nembedding with high fidelity to the underlying dynamics. We demonstrate how the\nVDE is able to capture nontrivial dynamics in a variety of examples, including\nBrownian dynamics and atomistic protein folding. Additionally, we demonstrate a\nmethod for analyzing the VDE model, inspired by saliency mapping, to determine\nwhat features are selected by the VDE model to describe dynamics. The VDE\npresents an important step in applying techniques from deep learning to more\naccurately model and interpret complex biophysics.","url_abs":"http://arxiv.org/abs/1711.08576v2","url_pdf":"http://arxiv.org/pdf/1711.08576v2.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-encoding-of-complex-dynamics","repo_url":"https://github.com/msmbuilder/vde","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"variational-encoding-of-complex-dynamics","repo_url":"https://github.com/msultan/vde_metadynamics","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"protein-folding","task_name":"Protein Folding"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.08576","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1711.08576"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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