CUQIpy: I. Computational uncertainty quantification for inverse problems in Python

作者：Nicolai A. B. Riis, Amal Alghamdi, Felipe Uribe, Silja Westphal Christensen, Babak Maboudi Afkham, Per Christian Hansen, Jakob Sauer Jørgensen · 发表于：Inverse Problems · 年份：2024 · DOI：10.1088/1361-6420/ad22e7 · 被引用次数：12 · 研究领域：Gaussian Processes and Bayesian Inference、Probabilistic and Robust Engineering Design、Model Reduction and Neural Networks

Abstract 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.