On the construction of non-intrusive multifidelity models for computer codes with time-series output: Comparison of three paradigms on a transient thermal problem.
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| Title: | On the construction of non-intrusive multifidelity models for computer codes with time-series output: Comparison of three paradigms on a transient thermal problem. |
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| Authors: | Nasser, H.1 (AUTHOR) hadi.nasser@univ-eiffel.fr, Perrin, G.1 (AUTHOR) guillaume.perrin@univ-eiffel.fr, Chakir, R.1 (AUTHOR) rachida.chakir@univ-eiffel.fr, Demeyer, S.2 (AUTHOR) severine.demeyer@lne.fr, Waeytens, J.1 (AUTHOR) julien.waeytens@univ-eiffel.fr |
| Source: | Journal of Computational Physics. Dec2025, Vol. 543, pN.PAG-N.PAG. 1p. |
| Subjects: | Gaussian processes, Long short-term memory, Reduced-order models, Forecasting, Thermal analysis |
| Abstract: | • Multifidelity approaches for predicting codes with functional outputs. • Overview of non-intrusive multifidelity techniques for simplified use. • Practical guideline for meta-model selection. • Comparative insights on multifidelity methods' benefits and limitations. • Comparison of methods on a 3D transient thermal problem. This study evaluates three multifidelity meta-modeling approaches for time-varying systems: the Non-Intrusive Reduced Basis method (NIRB), the Gaussian Process Regression (GPR), and the Long Short-Term Memory Recurrent Neural Networks (LSTM). The focus is on predicting, in a small data context, the output of an expensive numerical code (high-fidelity version) that takes a parameter vector as input and produces a time-dependent function as output, leveraging one (or more) less expensive code(s) referred as low-fidelity versions. The goal is to analyze the strengths and limitations of these approaches in this context, and to make these three approaches accessible, even to non-specialists. To this end, we compare these methods based on prediction performance (accuracy), ease of hyper-parameter tuning, offline versus online computational costs, ability to quantify uncertainty, but also based on their complexity of implementation. The analysis also explores the advantages of using multiple codes and strategies for distributing computational costs across each code. Numerical experiments are conducted using a thermal model designed to predict surface heat fluxes and temperatures of a multi-layer wall based on its geometry and the thermal properties of its materials. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Computational Physics is the property of Academic Press Inc. 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.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 188710124 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: On the construction of non-intrusive multifidelity models for computer codes with time-series output: Comparison of three paradigms on a transient thermal problem. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nasser%2C+H%2E%22">Nasser, H.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hadi.nasser@univ-eiffel.fr</i><br /><searchLink fieldCode="AR" term="%22Perrin%2C+G%2E%22">Perrin, G.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> guillaume.perrin@univ-eiffel.fr</i><br /><searchLink fieldCode="AR" term="%22Chakir%2C+R%2E%22">Chakir, R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rachida.chakir@univ-eiffel.fr</i><br /><searchLink fieldCode="AR" term="%22Demeyer%2C+S%2E%22">Demeyer, S.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> severine.demeyer@lne.fr</i><br /><searchLink fieldCode="AR" term="%22Waeytens%2C+J%2E%22">Waeytens, J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> julien.waeytens@univ-eiffel.fr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Computational+Physics%22">Journal of Computational Physics</searchLink>. Dec2025, Vol. 543, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Gaussian+processes%22">Gaussian processes</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Reduced-order+models%22">Reduced-order models</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Thermal+analysis%22">Thermal analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: • Multifidelity approaches for predicting codes with functional outputs. • Overview of non-intrusive multifidelity techniques for simplified use. • Practical guideline for meta-model selection. • Comparative insights on multifidelity methods' benefits and limitations. • Comparison of methods on a 3D transient thermal problem. This study evaluates three multifidelity meta-modeling approaches for time-varying systems: the Non-Intrusive Reduced Basis method (NIRB), the Gaussian Process Regression (GPR), and the Long Short-Term Memory Recurrent Neural Networks (LSTM). The focus is on predicting, in a small data context, the output of an expensive numerical code (high-fidelity version) that takes a parameter vector as input and produces a time-dependent function as output, leveraging one (or more) less expensive code(s) referred as low-fidelity versions. The goal is to analyze the strengths and limitations of these approaches in this context, and to make these three approaches accessible, even to non-specialists. To this end, we compare these methods based on prediction performance (accuracy), ease of hyper-parameter tuning, offline versus online computational costs, ability to quantify uncertainty, but also based on their complexity of implementation. The analysis also explores the advantages of using multiple codes and strategies for distributing computational costs across each code. Numerical experiments are conducted using a thermal model designed to predict surface heat fluxes and temperatures of a multi-layer wall based on its geometry and the thermal properties of its materials. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Computational Physics is the property of Academic Press Inc. 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: 10.1016/j.jcp.2025.114411 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Gaussian processes Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Reduced-order models Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Thermal analysis Type: general Titles: – TitleFull: On the construction of non-intrusive multifidelity models for computer codes with time-series output: Comparison of three paradigms on a transient thermal problem. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nasser, H. – PersonEntity: Name: NameFull: Perrin, G. – PersonEntity: Name: NameFull: Chakir, R. – PersonEntity: Name: NameFull: Demeyer, S. – PersonEntity: Name: NameFull: Waeytens, J. IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 00219991 Numbering: – Type: volume Value: 543 Titles: – TitleFull: Journal of Computational Physics Type: main |
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