Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2112.14837 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915471193800704 |
|---|---|
| author | Chharia, Aviral Mehta, Nishi Gupta, Shivam Prajapati, Shivam |
| author_facet | Chharia, Aviral Mehta, Nishi Gupta, Shivam Prajapati, Shivam |
| contents | The rise of computation-based methods in thermal management has gained immense attention in recent years due to the ability of deep learning to solve complex 'physics' problems, which are otherwise difficult to be approached using conventional techniques. Thermal management is required in electronic systems to keep them from overheating and burning, enhancing their efficiency and lifespan. For a long time, numerical techniques have been employed to aid in the thermal management of electronics. However, they come with some limitations. To increase the effectiveness of traditional numerical approaches and address the drawbacks faced in conventional approaches, researchers have looked at using artificial intelligence at various stages of the thermal management process. The present study discusses in detail, the current uses of deep learning in the domain of 'electronic' thermal management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_14837 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Recent Trends in Artificial Intelligence-inspired Electronic Thermal Management Chharia, Aviral Mehta, Nishi Gupta, Shivam Prajapati, Shivam Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science The rise of computation-based methods in thermal management has gained immense attention in recent years due to the ability of deep learning to solve complex 'physics' problems, which are otherwise difficult to be approached using conventional techniques. Thermal management is required in electronic systems to keep them from overheating and burning, enhancing their efficiency and lifespan. For a long time, numerical techniques have been employed to aid in the thermal management of electronics. However, they come with some limitations. To increase the effectiveness of traditional numerical approaches and address the drawbacks faced in conventional approaches, researchers have looked at using artificial intelligence at various stages of the thermal management process. The present study discusses in detail, the current uses of deep learning in the domain of 'electronic' thermal management. |
| title | Recent Trends in Artificial Intelligence-inspired Electronic Thermal Management |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2112.14837 |