{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T09:09:46Z","timestamp":1773047386806,"version":"3.50.1"},"reference-count":23,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","funder":[{"name":"ONR","award":["N00014-23-1-2106"],"award-info":[{"award-number":["N00014-23-1-2106"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Bifurcation Chaos"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:p> In this work, we present a method that determines optimal multistep Dynamic Mode Decomposition (DMD) models via Entropic Regression (ER), which is a nonlinear information flow detection algorithm. Motivated by the Higher-Order DMD (HODMD) method of [ Le Clainche &amp; Vega ,\u00a0 2017 ], and the ER technique for network detection and model construction found in [ Sun et al. ,\u00a0 2015 ;\u00a0 AlMomani et al. ,\u00a0 2020 ], we develop a method that we call ERDMD, which produces high fidelity time-delay DMD models that allow for nonuniformity in the delays. This optimal choice of delays is discovered by maximizing informativity as measured through ER. These models are shown to be highly efficient and robust. We test our method over several data sets generated by chaotic attractors and show that we are able to build excellent reconstructions using relatively minimal models. We likewise are able to better identify multiscale features via our models which enhances the utility of DMD. <\/jats:p>","DOI":"10.1142\/s0218127424501670","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T09:55:37Z","timestamp":1726653337000},"source":"Crossref","is-referenced-by-count":1,"title":["Entropic Regression Dynamic Mode Decomposition (ERDMD) Discovers Informative Sparse and Nonuniformly Time Delayed Models"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3492-1858","authenticated-orcid":false,"given":"Christopher W.","family":"Curtis","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7083-7592","authenticated-orcid":false,"given":"Erik","family":"Bollt","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Clarkson University, 8 Clarkson Avenue, Potsdam, New York 13699, USA"},{"name":"Clarkson Center for Complex Systems Science, Clarkson University, 8 Clarkson Avenue, Potsdam, New York 13699, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7196-944X","authenticated-orcid":false,"given":"Daniel Jay","family":"Alford-Lago","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, San Diego State University, 5500 Campanile Drive, San Diego, California 92182, USA"},{"name":"Naval Information Warfare Center, 49275 Electron Drive, San Diego, California 92152, USA"}]}],"member":"219","published-online":{"date-parts":[[2024,9,18]]},"reference":[{"key":"S0218127424501670BIB001","doi-asserted-by":"publisher","DOI":"10.1063\/5.0073893"},{"key":"S0218127424501670BIB002","doi-asserted-by":"publisher","DOI":"10.1063\/1.5133386"},{"key":"S0218127424501670BIB003","doi-asserted-by":"publisher","DOI":"10.1137\/17M1125236"},{"key":"S0218127424501670BIB004","first-page":"475","volume-title":"Proc. 37th Int. 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