Machine-Learning for Stress Tensor Modelling in Large Eddy Simulation (English)
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- New search for: Nikolaou, Z. M.
- New search for: Minamoto, Y.
- New search for: Chrysostomou, C.
- New search for: Vervisch, L.
- New search for: Swaminathan, Nedunchezhian
- New search for: Parente, Alessandro
- New search for: Nikolaou, Z. M.
- New search for: Minamoto, Y.
- New search for: Chrysostomou, C.
- New search for: Vervisch, L.
In:
Machine Learning and Its Application to Reacting Flows
: ML and Combustion
;
Chapter: 4
;
89-116
;
2023
- Article/Chapter (Book) / Electronic Resource
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Title:Machine-Learning for Stress Tensor Modelling in Large Eddy Simulation
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Additional title:Lect.Notes Energy
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Contributors:Swaminathan, Nedunchezhian ( editor ) / Parente, Alessandro ( editor ) / Nikolaou, Z. M. ( author ) / Minamoto, Y. ( author ) / Chrysostomou, C. ( author ) / Vervisch, L. ( author )
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Published in:Machine Learning and Its Application to Reacting Flows : ML and Combustion ; Chapter: 4 ; 89-116Lecture Notes in Energy ; 44 ; 89-116
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Publisher:
- New search for: Springer International Publishing
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Place of publication:Cham
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Publication date:2023-01-02
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Size:28 pages
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ISBN:
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ISSN:
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DOI:
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Type of media:Article/Chapter (Book)
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Type of material:Electronic Resource
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Language:English
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Keywords:
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Licence:
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Source:
Table of contents eBook
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
- 1
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IntroductionSwaminathan, N. / Parente, A. et al. | 2023
- 2
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Machine Learning Techniques in Reactive Atomistic SimulationsAktulga, H. / Ravindra, V. / Grama, A. / Pandit, S. et al. | 2023
- 3
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A Novel In Situ Machine Learning Framework for Intelligent Data Capture and Event DetectionShead, T. M. / Tezaur, I. K. / Davis IV, W. L. / Carlson, M. L. / Dunlavy, D. M. / Parish, E. J. / Blonigan, P. J. / Tencer, J. / Rizzi, F. / Kolla, H. et al. | 2023
- 4
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Machine-Learning for Stress Tensor Modelling in Large Eddy SimulationNikolaou, Z. M. / Minamoto, Y. / Chrysostomou, C. / Vervisch, L. et al. | 2023
- 5
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Machine Learning for Combustion ChemistryEchekki, T. / Farooq, A. / Ihme, M. / Sarathy, S. M. et al. | 2023
- 6
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Deep Convolutional Neural Networks for Subgrid-Scale Flame Wrinkling ModelingXing, V. / Lapeyre, C. J. et al. | 2023
- 7
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Machine Learning Strategy for Subgrid Modeling of Turbulent Combustion Using Linear Eddy Mixing Based TabulationRanjan, R. / Panchal, A. / Karpe, S. / Menon, S. et al. | 2023
- 8
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On the Use of Machine Learning for Subgrid Scale Filtered Density Function Modelling in Large Eddy Simulations of Combustion SystemsIavarone, S. / Yang, H. / Li, Z. / Chen, Z. X. / Swaminathan, N. et al. | 2023
- 9
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Reduced-Order Modeling of Reacting Flows Using Data-Driven ApproachesZdybał, K. / Malik, M. R. / Coussement, A. / Sutherland, J. C. / Parente, A. et al. | 2023
- 10
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AI Super-Resolution: Application to Turbulence and CombustionBode, M. et al. | 2023
- 11
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Machine Learning for ThermoacousticsJuniper, Matthew P. et al. | 2023