CUQIpy: I. Computational uncertainty quantification for inverse problems in Python
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909143978213376 |
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| author | Riis, Nicolai A B Alghamdi, Amal M A Uribe, Felipe Christensen, Silja L Afkham, Babak M Hansen, Per Christian Jørgensen, Jakob S |
| author_facet | Riis, Nicolai A B Alghamdi, Amal M A Uribe, Felipe Christensen, Silja L Afkham, Babak M Hansen, Per Christian Jørgensen, Jakob S |
| contents | This paper introduces CUQIpy, a versatile open-source Python package for computational uncertainty quantification (UQ) in inverse problems, presented as Part I of a two-part series. CUQIpy employs a Bayesian framework, integrating prior knowledge with observed data to produce posterior probability distributions that characterize the uncertainty in computed solutions to inverse problems. The package offers a high-level modeling framework with concise syntax, allowing users to easily specify their inverse problems, prior information, and statistical assumptions. CUQIpy supports a range of efficient sampling strategies and is designed to handle large-scale problems. Notably, the automatic sampler selection feature analyzes the problem structure and chooses a suitable sampler without user intervention, streamlining the process. With a selection of probability distributions, test problems, computational methods, and visualization tools, CUQIpy serves as a powerful, flexible, and adaptable tool for UQ in a wide selection of inverse problems. Part II of the series focuses on the use of CUQIpy for UQ in inverse problems with partial differential equations (PDEs). |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_16949 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CUQIpy: I. Computational uncertainty quantification for inverse problems in Python Riis, Nicolai A B Alghamdi, Amal M A Uribe, Felipe Christensen, Silja L Afkham, Babak M Hansen, Per Christian Jørgensen, Jakob S Numerical Analysis Mathematical Software 65R32, 65C20, 94A08, 65K10 G.3; I.4; G.1.0 This paper introduces CUQIpy, a versatile open-source Python package for computational uncertainty quantification (UQ) in inverse problems, presented as Part I of a two-part series. CUQIpy employs a Bayesian framework, integrating prior knowledge with observed data to produce posterior probability distributions that characterize the uncertainty in computed solutions to inverse problems. The package offers a high-level modeling framework with concise syntax, allowing users to easily specify their inverse problems, prior information, and statistical assumptions. CUQIpy supports a range of efficient sampling strategies and is designed to handle large-scale problems. Notably, the automatic sampler selection feature analyzes the problem structure and chooses a suitable sampler without user intervention, streamlining the process. With a selection of probability distributions, test problems, computational methods, and visualization tools, CUQIpy serves as a powerful, flexible, and adaptable tool for UQ in a wide selection of inverse problems. Part II of the series focuses on the use of CUQIpy for UQ in inverse problems with partial differential equations (PDEs). |
| title | CUQIpy: I. Computational uncertainty quantification for inverse problems in Python |
| topic | Numerical Analysis Mathematical Software 65R32, 65C20, 94A08, 65K10 G.3; I.4; G.1.0 |
| url | https://arxiv.org/abs/2305.16949 |