{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T12:25:44Z","timestamp":1730204744480,"version":"3.28.0"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T00:00:00Z","timestamp":1639440000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,14]]},"DOI":"10.1109\/cdc45484.2021.9683426","type":"proceedings-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T15:50:18Z","timestamp":1643730618000},"page":"2896-2896","source":"Crossref","is-referenced-by-count":4,"title":["Learning ODE Models with Qualitative Structure Using Gaussian Processes"],"prefix":"10.1109","author":[{"given":"Steffen","family":"Ridderbusch","sequence":"first","affiliation":[{"name":"University of Oxford,Control Group,Department of Engineering,Oxford,UK,OX1 3PJ"}]},{"given":"Christian","family":"Offen","sequence":"additional","affiliation":[{"name":"University of Paderborn,Department of Mathematics, Numerical Mathematics and Control,Paderborn,Germany,33098"}]},{"given":"Sina","family":"Ober-Blobaum","sequence":"additional","affiliation":[{"name":"University of Paderborn,Department of Mathematics, Numerical Mathematics and Control,Paderborn,Germany,33098"}]},{"given":"Paul","family":"Goulart","sequence":"additional","affiliation":[{"name":"University of Oxford,Control Group,Department of Engineering,Oxford,UK,OX1 3PJ"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84794-8"},{"journal-title":"Computer Algebra Methods for Equivariant Dynamical Systems","year":"2007","author":"gatermann","key":"ref11"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.84.041929"},{"key":"ref13","article-title":"Gaussian Processes for Machine Learning","author":"rasmussen","year":"2006","journal-title":"Adaptive Computation and Machine Learning"},{"key":"ref14","article-title":"ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems","author":"wenk","year":"2019","journal-title":"arXiv 1902 06278"},{"key":"ref15","article-title":"Kernels for Multi&#x2013;Task Learning","author":"micchelli","year":"2005","journal-title":"Advances in neural information processing systems"},{"key":"ref16","article-title":"Interpolation with Uncoupled Separable Matrix-Valued Kernels","author":"wittwar","year":"2018","journal-title":"Dolomites Research Notes on Approximation"},{"key":"ref17","article-title":"Learning Equivariant Functions with Matrix Valued Kernels","author":"reisert","year":"2007","journal-title":"Journal of Machine Learning Research"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.laa.2014.10.038"},{"key":"ref19","article-title":"A Unifying View of Sparse Approximate Gaussian Process Regression","author":"qui\u00f1onero-candela","year":"2005","journal-title":"J Mach Learn Res"},{"key":"ref4","doi-asserted-by":"crossref","DOI":"10.1086\/289911","article-title":"Emergence and Strange Attractors","author":"newman","year":"1996","journal-title":"Phi-losophy of Science"},{"key":"ref3","article-title":"Actively Learning Gaussian Process Dynamics","author":"buisson-fenet","year":"2020","journal-title":"Proceedings of the 2nd Conference on Learning for Dynamics and Control Proceedings of Machine Learning Research The Cloud PMLR"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1517384113"},{"key":"ref5","doi-asserted-by":"crossref","DOI":"10.21203\/rs.3.rs-55125\/v1","article-title":"Universal Differential Equations for Scientific Machine Learning","author":"rackauckas","year":"2020"},{"key":"ref8","article-title":"Learning Dynamical Systems with Side Information","author":"ahmadi","year":"2020","journal-title":"Proceedings of Machine Learning Research The Cloud PMLR"},{"article-title":"Learning Unknown ODE Models with Gaussian Processes","year":"2018","author":"heinonen","key":"ref7"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2013.218","article-title":"Gaussian Processes for Data-Efficient Learning in Robotics and Control","author":"deisenroth","year":"2015","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s00498-019-00246-7"},{"article-title":"Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control","year":"2017","author":"kamthe","key":"ref1"},{"key":"ref20","article-title":"Sparse Gaussian Processes Using Pseudo-Inputs","volume":"18","author":"snelson","year":"2006","journal-title":"Advances in neural information processing systems"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1093\/imanum\/drv062"},{"key":"ref21","article-title":"Gaussian Process Training with Input Noise","author":"mchutchon","year":"2011","journal-title":"Proceedings of the 24th International Conference on Neural Information Processing Systems NIPS&#x2019;11"},{"key":"ref24","article-title":"Online Sparse Gaussian Process Regression Using FITC and PITC Approximations","author":"bijl","year":"2015","journal-title":"IFAC-PapersOnLine 17th IFAC Symposium on System Identification SYSID 2015"},{"key":"ref23","article-title":"On Simulation and Trajectory Prediction with Gaussian Process Dynamics","author":"hewing","year":"2020","journal-title":"Proceedings of the 2nd Conference on Learning for Dynamics and Control Proceedings of Machine Learning Research The Cloud PMLR"},{"article-title":"Geometric Numerical Integration: Structure-Preserving Algorithms for Ordinary Differential Equations","year":"2006","author":"hairer","key":"ref25"}],"event":{"name":"2021 60th IEEE Conference on Decision and Control (CDC)","start":{"date-parts":[[2021,12,14]]},"location":"Austin, TX, USA","end":{"date-parts":[[2021,12,17]]}},"container-title":["2021 60th IEEE Conference on Decision and Control (CDC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9682670\/9682776\/09683426.pdf?arnumber=9683426","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T16:24:59Z","timestamp":1654532699000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9683426\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,14]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/cdc45484.2021.9683426","relation":{},"subject":[],"published":{"date-parts":[[2021,12,14]]}}}