تقرير
Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data
| العنوان: | Physics-Informed Neural Networks for Dynamic Process Operations with Limited Physical Knowledge and Data |
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| المؤلفون: | Velioglu, Mehmet, Zhai, Song, Rupprecht, Sophia, Mitsos, Alexander, Jupke, Andreas, Dahmen, Manuel |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | In chemical engineering, process data are expensive to acquire, and complex phenomena are difficult to fully model. We explore the use of physics-informed neural networks (PINNs) for dynamic processes with incomplete mechanistic semi-explicit differential-algebraic equation systems and scarce process data. In particular, we focus on estimating states for which neither direct observational data nor constitutive equations are available. We propose an easy-to-apply heuristic to assess whether estimation of such states may be possible. As numerical examples, we consider a continuously stirred tank reactor and a liquid-liquid separator. We find that PINNs can infer unmeasured states with reasonable accuracy, and they generalize better in low-data scenarios than purely data-driven models. We thus show that PINNs are capable of modeling processes when relatively few experimental data and only partially known mechanistic descriptions are available, and conclude that they constitute a promising avenue that warrants further investigation. Comment: manuscript (32 pages, 9 figures, 11 tables), supporting materials (14 pages, 4 figures, 5 tables) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.01528 |
| رقم الأكسشن: | edsarx.2406.01528 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |