Academic Journal
HyperNOs: automated and parallel library for neural operators research.
| Τίτλος: | HyperNOs: automated and parallel library for neural operators research. |
|---|---|
| Συγγραφείς: | Ghiotto, Massimiliano |
| Πηγή: | Bollettino dell'Unione Matematica Italiana; Sep2026, Vol. 19 Issue 3, p709-743, 35p |
| Θεματικοί όροι: | Software libraries (Computer programming), Machine learning, Parallel programming, Convolutional neural networks |
| Περίληψη: | This paper introduces HyperNOs, a PyTorch library designed to streamline and automate the process of exploring neural operators, with a special focus on hyperparameter optimization for comprehensive and exhaustive exploration. In particular, HyperNOs leverages state-of-the-art optimization algorithms and parallel computing implemented in the Ray-tune library to efficiently explore the hyperparameter space of neural operators. We also implement several features for studying neural operators with a user-friendly interface, such as the ability to train the model with a fixed number of parameters or to train the model with multiple datasets and different resolutions. We integrate Fourier neural operators and convolutional neural operators in our library, achieving state-of-the-art results on many representative benchmarks, demonstrating the capabilities of HyperNOs to handle real datasets and modern architectures. The library is designed for ease of use with the provided model and datasets, but also to be extended to use new datasets and custom neural operator architectures. [ABSTRACT FROM AUTHOR] |
| Copyright of Bollettino dell'Unione Matematica Italiana is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s40574-025-00516-0 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edb DbLabel: Complementary Index An: 196299879 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42749023438 |
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| Items | – Name: Title Label: Title Group: Ti Data: HyperNOs: automated and parallel library for neural operators research. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ghiotto%2C+Massimiliano%22">Ghiotto, Massimiliano</searchLink> – Name: TitleSource Label: Source Group: Src Data: Bollettino dell'Unione Matematica Italiana; Sep2026, Vol. 19 Issue 3, p709-743, 35p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Software+libraries+%28Computer+programming%29%22">Software libraries (Computer programming)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programming%22">Parallel programming</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper introduces HyperNOs, a PyTorch library designed to streamline and automate the process of exploring neural operators, with a special focus on hyperparameter optimization for comprehensive and exhaustive exploration. In particular, HyperNOs leverages state-of-the-art optimization algorithms and parallel computing implemented in the Ray-tune library to efficiently explore the hyperparameter space of neural operators. We also implement several features for studying neural operators with a user-friendly interface, such as the ability to train the model with a fixed number of parameters or to train the model with multiple datasets and different resolutions. We integrate Fourier neural operators and convolutional neural operators in our library, achieving state-of-the-art results on many representative benchmarks, demonstrating the capabilities of HyperNOs to handle real datasets and modern architectures. The library is designed for ease of use with the provided model and datasets, but also to be extended to use new datasets and custom neural operator architectures. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Bollettino dell'Unione Matematica Italiana is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s40574-025-00516-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 35 StartPage: 709 Subjects: – SubjectFull: Software libraries (Computer programming) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Parallel programming Type: general – SubjectFull: Convolutional neural networks Type: general Titles: – TitleFull: HyperNOs: automated and parallel library for neural operators research. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ghiotto, Massimiliano IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19726724 Numbering: – Type: volume Value: 19 – Type: issue Value: 3 Titles: – TitleFull: Bollettino dell'Unione Matematica Italiana Type: main |
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