On Fostering Predictions in Data-Driven Reduced Order Model for Eulerian–Lagrangian Simulations: Decision of Sufficient Training Data
The development of a data-driven surrogate model (SM) is extensively studied in Eulerian–Lagrangian simulations for its advantage of high computational speed. However, in the application of granular systems with violent fluid-solid flows, how to select sufficient training data to ensure consistency...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Journal of Chemical Engineering of Japan |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00219592.2024.2316155 |
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| author | Kai-en Yang Shuo Li Guangtao Duan Mikio Sakai |
| author_facet | Kai-en Yang Shuo Li Guangtao Duan Mikio Sakai |
| author_sort | Kai-en Yang |
| collection | DOAJ |
| description | The development of a data-driven surrogate model (SM) is extensively studied in Eulerian–Lagrangian simulations for its advantage of high computational speed. However, in the application of granular systems with violent fluid-solid flows, how to select sufficient training data to ensure consistency between the high-fidelity model and SM remains unknown and highly challenging. The accuracy of SM can be easily deteriorated due to insufficient training data. This necessitates a trial-and-error process and hinders its industrial applications. To address this issue, this study newly reveals a finding that data density is a key to sufficient training, and we propose a novel technique for deciding the sufficient training data of SM. Specifically, a feasibility index is proposed based on posterior error analysis. It is demonstrated that when the training data is determined under the proposed feasibility index [Formula: see text] 2, the consistency of granular dynamics between SM and the high-fidelity model can be guaranteed. Employed in a representative SM, a reduced order model (ROM), this technique enables the successful decision of sufficient training data, resulting in the remarkable predictability in violent fluid-solid flows without trial-and-error. This technique holds great potential in solving the predicament of deciding training data for data-driven models. |
| format | Article |
| id | doaj-art-c5da0f4de59a41479cc881a7d95093fc |
| institution | OA Journals |
| issn | 0021-9592 1881-1299 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Chemical Engineering of Japan |
| spelling | doaj-art-c5da0f4de59a41479cc881a7d95093fc2025-08-20T02:34:35ZengTaylor & Francis GroupJournal of Chemical Engineering of Japan0021-95921881-12992024-12-0157110.1080/00219592.2024.2316155On Fostering Predictions in Data-Driven Reduced Order Model for Eulerian–Lagrangian Simulations: Decision of Sufficient Training DataKai-en Yang0Shuo Li1Guangtao Duan2Mikio Sakai3Department of Nuclear Engineering and Management, The University of Tokyo, Tokyo, JapanDepartment of Nuclear Engineering and Management, The University of Tokyo, Tokyo, JapanDepartment of Nuclear Engineering and Management, The University of Tokyo, Tokyo, JapanDepartment of Nuclear Engineering and Management, The University of Tokyo, Tokyo, JapanThe development of a data-driven surrogate model (SM) is extensively studied in Eulerian–Lagrangian simulations for its advantage of high computational speed. However, in the application of granular systems with violent fluid-solid flows, how to select sufficient training data to ensure consistency between the high-fidelity model and SM remains unknown and highly challenging. The accuracy of SM can be easily deteriorated due to insufficient training data. This necessitates a trial-and-error process and hinders its industrial applications. To address this issue, this study newly reveals a finding that data density is a key to sufficient training, and we propose a novel technique for deciding the sufficient training data of SM. Specifically, a feasibility index is proposed based on posterior error analysis. It is demonstrated that when the training data is determined under the proposed feasibility index [Formula: see text] 2, the consistency of granular dynamics between SM and the high-fidelity model can be guaranteed. Employed in a representative SM, a reduced order model (ROM), this technique enables the successful decision of sufficient training data, resulting in the remarkable predictability in violent fluid-solid flows without trial-and-error. This technique holds great potential in solving the predicament of deciding training data for data-driven models.https://www.tandfonline.com/doi/10.1080/00219592.2024.2316155Data-driven ROMFluid-solid flowsSufficient training dataPredictions |
| spellingShingle | Kai-en Yang Shuo Li Guangtao Duan Mikio Sakai On Fostering Predictions in Data-Driven Reduced Order Model for Eulerian–Lagrangian Simulations: Decision of Sufficient Training Data Journal of Chemical Engineering of Japan Data-driven ROM Fluid-solid flows Sufficient training data Predictions |
| title | On Fostering Predictions in Data-Driven Reduced Order Model for Eulerian–Lagrangian Simulations: Decision of Sufficient Training Data |
| title_full | On Fostering Predictions in Data-Driven Reduced Order Model for Eulerian–Lagrangian Simulations: Decision of Sufficient Training Data |
| title_fullStr | On Fostering Predictions in Data-Driven Reduced Order Model for Eulerian–Lagrangian Simulations: Decision of Sufficient Training Data |
| title_full_unstemmed | On Fostering Predictions in Data-Driven Reduced Order Model for Eulerian–Lagrangian Simulations: Decision of Sufficient Training Data |
| title_short | On Fostering Predictions in Data-Driven Reduced Order Model for Eulerian–Lagrangian Simulations: Decision of Sufficient Training Data |
| title_sort | on fostering predictions in data driven reduced order model for eulerian lagrangian simulations decision of sufficient training data |
| topic | Data-driven ROM Fluid-solid flows Sufficient training data Predictions |
| url | https://www.tandfonline.com/doi/10.1080/00219592.2024.2316155 |
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