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Deep kernel learning of dynamical models from high-dimensional noisy data
This work proposes a stochastic variational deep kernel learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables, and...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9747975/ https://www.ncbi.nlm.nih.gov/pubmed/36513711 http://dx.doi.org/10.1038/s41598-022-25362-4 |
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author | Botteghi, Nicolò Guo, Mengwu Brune, Christoph |
author_facet | Botteghi, Nicolò Guo, Mengwu Brune, Christoph |
author_sort | Botteghi, Nicolò |
collection | PubMed |
description | This work proposes a stochastic variational deep kernel learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables, and a latent dynamical model for the state variables that predicts the system evolution over time. The training of the proposed model is carried out in an unsupervised manner, i.e., not relying on labeled data. Our learning method is evaluated on the motion of a pendulum—a well studied baseline for nonlinear model identification and control with continuous states and control inputs—measured via high-dimensional noisy RGB images. Results show that the method can effectively denoise measurements, learn compact state representations and latent dynamical models, as well as identify and quantify modeling uncertainties. |
format | Online Article Text |
id | pubmed-9747975 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97479752022-12-15 Deep kernel learning of dynamical models from high-dimensional noisy data Botteghi, Nicolò Guo, Mengwu Brune, Christoph Sci Rep Article This work proposes a stochastic variational deep kernel learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables, and a latent dynamical model for the state variables that predicts the system evolution over time. The training of the proposed model is carried out in an unsupervised manner, i.e., not relying on labeled data. Our learning method is evaluated on the motion of a pendulum—a well studied baseline for nonlinear model identification and control with continuous states and control inputs—measured via high-dimensional noisy RGB images. Results show that the method can effectively denoise measurements, learn compact state representations and latent dynamical models, as well as identify and quantify modeling uncertainties. Nature Publishing Group UK 2022-12-13 /pmc/articles/PMC9747975/ /pubmed/36513711 http://dx.doi.org/10.1038/s41598-022-25362-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Botteghi, Nicolò Guo, Mengwu Brune, Christoph Deep kernel learning of dynamical models from high-dimensional noisy data |
title | Deep kernel learning of dynamical models from high-dimensional noisy data |
title_full | Deep kernel learning of dynamical models from high-dimensional noisy data |
title_fullStr | Deep kernel learning of dynamical models from high-dimensional noisy data |
title_full_unstemmed | Deep kernel learning of dynamical models from high-dimensional noisy data |
title_short | Deep kernel learning of dynamical models from high-dimensional noisy data |
title_sort | deep kernel learning of dynamical models from high-dimensional noisy data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9747975/ https://www.ncbi.nlm.nih.gov/pubmed/36513711 http://dx.doi.org/10.1038/s41598-022-25362-4 |
work_keys_str_mv | AT botteghinicolo deepkernellearningofdynamicalmodelsfromhighdimensionalnoisydata AT guomengwu deepkernellearningofdynamicalmodelsfromhighdimensionalnoisydata AT brunechristoph deepkernellearningofdynamicalmodelsfromhighdimensionalnoisydata |