Topologically consistent regression modeling exemplified for laminar burning velocity of ammonia-hydrogen flames
Data-driven regression models are generally calibrated by minimizing a representation error. However, optimizing the model accuracy may create nonphysical wiggles. In this study, we propose topological consistency as a new metric to mitigate these wiggles. The key enabler is Persistent Data Topology...
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| Главные авторы: | , , , , , |
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| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Elsevier
2025-01-01
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| Серии: | Energy and AI |
| Предметы: | |
| Online-ссылка: | http://www.sciencedirect.com/science/article/pii/S2666546824001228 |
| Метки: |
Нет меток, Требуется 1-ая метка записи!
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