تقرير
Physics-informed neural networks for PDE-constrained optimization and control
| العنوان: | Physics-informed neural networks for PDE-constrained optimization and control |
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| المؤلفون: | Barry-Straume, Jostein, Sarshar, Arash, Popov, Andrey A., Sandu, Adrian |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Mathematics - Optimization and Control, I.2.6, I.2.8, I.5.1, G.1.6, G.1.8 |
| الوصف: | A fundamental problem in science and engineering is designing optimal control policies that steer a given system towards a desired outcome. This work proposes Control Physics-Informed Neural Networks (Control PINNs) that simultaneously solve for a given system state, and for the optimal control signal, in a one-stage framework that conforms to the underlying physical laws. Prior approaches use a two-stage framework that first models and then controls a system in sequential order. In contrast, a Control PINN incorporates the required optimality conditions in its architecture and in its loss function. The success of Control PINNs is demonstrated by solving the following open-loop optimal control problems: (i) an analytical problem, (ii) a one-dimensional heat equation, and (iii) a two-dimensional predator-prey problem. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2205.03377 |
| رقم الأكسشن: | edsarx.2205.03377 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |