دورية أكاديمية
Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook
| العنوان: | Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook |
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| المؤلفون: | Xuan Di, Rongye Shi, Zhaobin Mo, Yongjie Fu |
| المصدر: | Algorithms, Vol 16, Iss 6, p 305 (2023) |
| بيانات النشر: | MDPI AG, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Industrial engineering. Management engineering LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | physics-informed deep learning (PIDL), computational graph, uncertainty quantification, Industrial engineering. Management engineering, T55.4-60.8, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In other words, how the physics is encoded into DNNs and how the physics and data components are represented. In this paper, we offer an overview of a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE), a central problem in transportation engineering. When observation data, problem type, and goal vary, we demonstrate potential architectures of PIDL computational graphs and compare these variants using the same real-world dataset. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1999-4893 |
| Relation: | https://www.mdpi.com/1999-4893/16/6/305; https://doaj.org/toc/1999-4893 |
| DOI: | 10.3390/a16060305 |
| URL الوصول: | https://doaj.org/article/7d79cf8e00a2417c98c286cb90c0faae |
| رقم الأكسشن: | edsdoj.7d79cf8e00a2417c98c286cb90c0faae |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 19994893 |
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| DOI: | 10.3390/a16060305 |