Digital Twins in Industrial Applications: Concepts, Mathematical Modeling, and Use Cases
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866908453625135104 |
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| author | Mohammad-Djafari, Ali |
| author_facet | Mohammad-Djafari, Ali |
| contents | Digital Twins (DTs) are virtual representations of physical systems synchronized in real time through Internet of Things (IoT) sensors and computational models. In industrial applications, DTs enable predictive maintenance, fault diagnosis, and process optimization. This paper explores the mathematical foundations of DTs, hybrid modeling techniques, including Physics Informed Neural Networks (PINNs), and their implementation in industrial scenarios. We present key applications, computational tools, and future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12468 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Digital Twins in Industrial Applications: Concepts, Mathematical Modeling, and Use Cases Mohammad-Djafari, Ali Other Computer Science Digital Twins (DTs) are virtual representations of physical systems synchronized in real time through Internet of Things (IoT) sensors and computational models. In industrial applications, DTs enable predictive maintenance, fault diagnosis, and process optimization. This paper explores the mathematical foundations of DTs, hybrid modeling techniques, including Physics Informed Neural Networks (PINNs), and their implementation in industrial scenarios. We present key applications, computational tools, and future research directions. |
| title | Digital Twins in Industrial Applications: Concepts, Mathematical Modeling, and Use Cases |
| topic | Other Computer Science |
| url | https://arxiv.org/abs/2507.12468 |