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Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN

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Bibliographic Details
Published in:Journal of Machine Learning Research
Format: Online Article RSS Article
Published: 2026
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container_title Journal of Machine Learning Research
description
discipline_display Engineering & Technology
discipline_facet Engineering & Technology
format Online Article
RSS Article
genre Journal Article
id rss_article:4813
institution FRELIP
journal_source_facet Journal of Machine Learning Research
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN
title_auth Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN
title_full Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN
title_fullStr Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN
title_full_unstemmed Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN
title_short Physics Informed Kolmogorov-Arnold Neural Networks for Dynamical Analysis via Efficient-KAN and WAV-KAN
title_sort physics informed kolmogorov-arnold neural networks for dynamical analysis via efficient-kan and wav-kan
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url http://jmlr.org/papers/v26/24-1278.html