تقرير
Approximating electromagnetic fields in discontinuous media using a single physics-informed neural network
| العنوان: | Approximating electromagnetic fields in discontinuous media using a single physics-informed neural network |
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| المؤلفون: | Nohra, Michel, Dufour, Steven |
| سنة النشر: | 2024 |
| المجموعة: | Mathematics Mathematical Physics Physics (Other) |
| مصطلحات موضوعية: | Physics - Computational Physics, Mathematical Physics |
| الوصف: | Physics-Informed Neural Networks (PINNs) are a new family of numerical methods, based on deep learning, for modeling boundary value problems. They offer an advantage over traditional numerical methods for high-dimensional, parametric, and data-driven problems. However, they perform poorly on problems where the solution exhibits high frequencies, such as discontinuities or sharp gradients. In this work, we develop a PINN-based solver for modeling three-dimensional, transient and static, parametric electromagnetic problems in discontinuous media. We use the first-order Maxwell's equations to train the neural network. We use a level-set function to represent the interface with a continuous function, and to enrich the network's inputs with high-frequencies and interface information. Finally, we validate the proposed methodology on multiple 3D, parametric, static, and transient problems. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.20833 |
| رقم الأكسشن: | edsarx.2407.20833 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |