تقرير
Physics-Informed Machine Learning for Optical Modes in Composites
| العنوان: | Physics-Informed Machine Learning for Optical Modes in Composites |
|---|---|
| المؤلفون: | Ghosh, Abantika, Elhamod, Mohannad, Bu, Jie, Lee, Wei-Cheng, Karpatne, Anuj, Podolskiy, Viktor A |
| سنة النشر: | 2021 |
| المجموعة: | Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Physics - Computational Physics, Condensed Matter - Materials Science, Physics - Applied Physics, Physics - Optics |
| الوصف: | We demonstrate that embedding physics-driven constraints into machine learning process can dramatically improve accuracy and generalizability of the resulting model. Physics-informed learning is illustrated on the example of analysis of optical modes propagating through a spatially periodic composite. The approach presented can be readily utilized in other situations mapped onto an eigenvalue problem, a known bottleneck of computational electrodynamics. Physics-informed learning can be used to improve machine-learning-driven design, optimization, and characterization, in particular in situations where exact solutions are scarce or are slow to come up with. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2112.07625 |
| رقم الأكسشن: | edsarx.2112.07625 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |