CUQIpy: II. Computational uncertainty quantification for PDE-based 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_ | 1866914721744027648 |
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| author | Alghamdi, Amal M A Riis, Nicolai A B Afkham, Babak M Uribe, Felipe Christensen, Silja L Hansen, Per Christian Jørgensen, Jakob S |
| author_facet | Alghamdi, Amal M A Riis, Nicolai A B Afkham, Babak M Uribe, Felipe Christensen, Silja L Hansen, Per Christian Jørgensen, Jakob S |
| contents | Inverse problems, particularly those governed by Partial Differential Equations (PDEs), are prevalent in various scientific and engineering applications, and uncertainty quantification (UQ) of solutions to these problems is essential for informed decision-making. This second part of a two-paper series builds upon the foundation set by the first part, which introduced CUQIpy, a Python software package for computational UQ in inverse problems using a Bayesian framework. In this paper, we extend CUQIpy's capabilities to solve PDE-based Bayesian inverse problems through a general framework that allows the integration of PDEs in CUQIpy, whether expressed natively or using third-party libraries such as FEniCS. CUQIpy offers concise syntax that closely matches mathematical expressions, streamlining the modeling process and enhancing the user experience. The versatility and applicability of CUQIpy to PDE-based Bayesian inverse problems are demonstrated on examples covering parabolic, elliptic and hyperbolic PDEs. This includes problems involving the heat and Poisson equations and application case studies in electrical impedance tomography and photo-acoustic tomography, showcasing the software's efficiency, consistency, and intuitive interface. This comprehensive approach to UQ in PDE-based inverse problems provides accessibility for non-experts and advanced features for experts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_16951 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CUQIpy: II. Computational uncertainty quantification for PDE-based inverse problems in Python Alghamdi, Amal M A Riis, Nicolai A B Afkham, Babak M Uribe, Felipe Christensen, Silja L Hansen, Per Christian Jørgensen, Jakob S Numerical Analysis Mathematical Software 65R32, 65C20, 94A08, 65K10, 65M32 G.3; G.1.8 Inverse problems, particularly those governed by Partial Differential Equations (PDEs), are prevalent in various scientific and engineering applications, and uncertainty quantification (UQ) of solutions to these problems is essential for informed decision-making. This second part of a two-paper series builds upon the foundation set by the first part, which introduced CUQIpy, a Python software package for computational UQ in inverse problems using a Bayesian framework. In this paper, we extend CUQIpy's capabilities to solve PDE-based Bayesian inverse problems through a general framework that allows the integration of PDEs in CUQIpy, whether expressed natively or using third-party libraries such as FEniCS. CUQIpy offers concise syntax that closely matches mathematical expressions, streamlining the modeling process and enhancing the user experience. The versatility and applicability of CUQIpy to PDE-based Bayesian inverse problems are demonstrated on examples covering parabolic, elliptic and hyperbolic PDEs. This includes problems involving the heat and Poisson equations and application case studies in electrical impedance tomography and photo-acoustic tomography, showcasing the software's efficiency, consistency, and intuitive interface. This comprehensive approach to UQ in PDE-based inverse problems provides accessibility for non-experts and advanced features for experts. |
| title | CUQIpy: II. Computational uncertainty quantification for PDE-based inverse problems in Python |
| topic | Numerical Analysis Mathematical Software 65R32, 65C20, 94A08, 65K10, 65M32 G.3; G.1.8 |
| url | https://arxiv.org/abs/2305.16951 |