التفاصيل البيبلوغرافية
| العنوان: |
AI Pontryagin or how artificial neural networks learn to control dynamical systems |
| المؤلفون: |
Lucas Böttcher, Nino Antulov-Fantulin, Thomas Asikis |
| المصدر: |
Nature Communications, Vol 13, Iss 1, Pp 1-9 (2022) |
| بيانات النشر: |
Nature Portfolio, 2022. |
| سنة النشر: |
2022 |
| المجموعة: |
LCC:Science |
| مصطلحات موضوعية: |
Science |
| الوصف: |
Optimal control of complex dynamical systems can be challenging due to cost constraints and analytical intractability. The authors propose a machine-learning-based control framework able to learn control signals and force complex high-dimensional dynamical systems towards a desired target state. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2041-1723 |
| Relation: |
https://doaj.org/toc/2041-1723 |
| DOI: |
10.1038/s41467-021-27590-0 |
| URL الوصول: |
https://doaj.org/article/2ea92b9319604a4b841d9d3c141cf680 |
| رقم الانضمام: |
edsdoj.2ea92b9319604a4b841d9d3c141cf680 |
| قاعدة البيانات: |
Directory of Open Access Journals |
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