{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T19:41:44Z","timestamp":1770579704467,"version":"3.49.0"},"reference-count":23,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["42130607"],"award-info":[{"award-number":["42130607"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key R&D Program of China","award":["2023YFF0805100"],"award-info":[{"award-number":["2023YFF0805100"]}]},{"name":"Laoshan Laboratory","award":["LSKJ202202600"],"award-info":[{"award-number":["LSKJ202202600"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accurately predicting chaotic dynamical systems is a crucial task in various fields, and recent advancements have leveraged deep learning for this purpose. However, in the era of big data, the inevitable challenge of data contamination caused by invalid information from other interfering systems becomes increasingly prominent and complicates accurate predictions. Although contemporary deep learning methods have shown their potential, very few studies have focused on developing algorithms specifically designed to address the problem posed by such data contamination. Thus, exploring the ability of contemporary deep learning methods to purify polluted information fills an important gap in the current body of research. This study explores the performance and stability of several modern deep learning techniques for predicting chaotic systems using datasets polluted by invalid information. Our findings reveal that while most of the state-of-the-art deep learning methods exhibit reduced and unstable predictive performance owing to such contamination, the dynamical system deep learning (DSDL) method stands out, remaining unaffected by any interference. This breakthrough illustrates DSDL\u2019s unique ability to purify invalid data and uncover the inherent rules of chaotic systems. As we move forward, DSDL paves the way for a more reliable and interpretable model, ensuring that we can confidently predict chaotic systems with precision even in the most challenging environments.<\/jats:p>","DOI":"10.1088\/2632-2153\/ad8983","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T23:00:46Z","timestamp":1729551646000},"page":"045026","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Exploring the potential of contemporary deep learning methods in purifying polluted information"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3246-1464","authenticated-orcid":true,"given":"Mingyu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0625-1575","authenticated-orcid":true,"given":"Jianping","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"mlstad8983bib1","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1175\/1520-0477(1993)074<0049:ERAPAT>2.0.CO;2","article-title":"Extended-range atmospheric prediction and the Lorenz model","volume":"74","author":"Palmer","year":"1993","journal-title":"Bull. 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