Conformal Prediction on Quantifying Uncertainty of Dynamic Systems
Fuente:
arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909431357243392 |
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| author | Liang, Aoming Liu, Qi Xu, Lei Sohrab, Fahad Cui, Weicheng Song, Changhui Gabbouj, Moncef |
| author_facet | Liang, Aoming Liu, Qi Xu, Lei Sohrab, Fahad Cui, Weicheng Song, Changhui Gabbouj, Moncef |
| contents | Numerous studies have focused on learning and understanding the dynamics of physical systems from video data, such as spatial intelligence. Artificial intelligence requires quantitative assessments of the uncertainty of the model to ensure reliability. However, there is still a relative lack of systematic assessment of the uncertainties, particularly the uncertainties of the physical data. Our motivation is to introduce conformal prediction into the uncertainty assessment of dynamical systems, providing a method supported by theoretical guarantees. This paper uses the conformal prediction method to assess uncertainties with benchmark operator learning methods. We have also compared the Monte Carlo Dropout and Ensemble methods in the partial differential equations dataset, effectively evaluating uncertainty through straight roll-outs, making it ideal for time-series tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_10459 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Conformal Prediction on Quantifying Uncertainty of Dynamic Systems Liang, Aoming Liu, Qi Xu, Lei Sohrab, Fahad Cui, Weicheng Song, Changhui Gabbouj, Moncef Machine Learning Artificial Intelligence Numerous studies have focused on learning and understanding the dynamics of physical systems from video data, such as spatial intelligence. Artificial intelligence requires quantitative assessments of the uncertainty of the model to ensure reliability. However, there is still a relative lack of systematic assessment of the uncertainties, particularly the uncertainties of the physical data. Our motivation is to introduce conformal prediction into the uncertainty assessment of dynamical systems, providing a method supported by theoretical guarantees. This paper uses the conformal prediction method to assess uncertainties with benchmark operator learning methods. We have also compared the Monte Carlo Dropout and Ensemble methods in the partial differential equations dataset, effectively evaluating uncertainty through straight roll-outs, making it ideal for time-series tasks. |
| title | Conformal Prediction on Quantifying Uncertainty of Dynamic Systems |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2412.10459 |