PAPM: A Physics-aware Proxy Model for Process Systems
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914860678250496 |
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| author | Liu, Pengwei Hao, Zhongkai Ren, Xingyu Yuan, Hangjie Ren, Jiayang Ni, Dong |
| author_facet | Liu, Pengwei Hao, Zhongkai Ren, Xingyu Yuan, Hangjie Ren, Jiayang Ni, Dong |
| contents | In the context of proxy modeling for process systems, traditional data-driven deep learning approaches frequently encounter significant challenges, such as substantial training costs induced by large amounts of data, and limited generalization capabilities. As a promising alternative, physics-aware models incorporate partial physics knowledge to ameliorate these challenges. Although demonstrating efficacy, they fall short in terms of exploration depth and universality. To address these shortcomings, we introduce a physics-aware proxy model (PAPM) that fully incorporates partial prior physics of process systems, which includes multiple input conditions and the general form of conservation relations, resulting in better out-of-sample generalization. Additionally, PAPM contains a holistic temporal-spatial stepping module for flexible adaptation across various process systems. Through systematic comparisons with state-of-the-art pure data-driven and physics-aware models across five two-dimensional benchmarks in nine generalization tasks, PAPM notably achieves an average performance improvement of 6.7%, while requiring fewer FLOPs, and just 1% of the parameters compared to the prior leading method. The code is available at https://github.com/pengwei07/PAPM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05232 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PAPM: A Physics-aware Proxy Model for Process Systems Liu, Pengwei Hao, Zhongkai Ren, Xingyu Yuan, Hangjie Ren, Jiayang Ni, Dong Machine Learning In the context of proxy modeling for process systems, traditional data-driven deep learning approaches frequently encounter significant challenges, such as substantial training costs induced by large amounts of data, and limited generalization capabilities. As a promising alternative, physics-aware models incorporate partial physics knowledge to ameliorate these challenges. Although demonstrating efficacy, they fall short in terms of exploration depth and universality. To address these shortcomings, we introduce a physics-aware proxy model (PAPM) that fully incorporates partial prior physics of process systems, which includes multiple input conditions and the general form of conservation relations, resulting in better out-of-sample generalization. Additionally, PAPM contains a holistic temporal-spatial stepping module for flexible adaptation across various process systems. Through systematic comparisons with state-of-the-art pure data-driven and physics-aware models across five two-dimensional benchmarks in nine generalization tasks, PAPM notably achieves an average performance improvement of 6.7%, while requiring fewer FLOPs, and just 1% of the parameters compared to the prior leading method. The code is available at https://github.com/pengwei07/PAPM. |
| title | PAPM: A Physics-aware Proxy Model for Process Systems |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2407.05232 |