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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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Journal of Chemical Engineering of Japan |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00219592.2024.2316155 |
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