Solving inverse problems using data-driven models
Solving inverse problems using data-driven models
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DOI：
10.1017/s0962492919000059
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发表时间：
2019-01-01
期刊：
影响因子：
14.2
通讯作者：
Schonlieb, Carola-Bibiane
中科院分区：
文献类型：
--
作者：
Arridge, Simon;Maass, Peter;Schonlieb, Carola-Bibiane
最近的反问题研究试图建立一个数学上连贯的基础，将数据驱动模型，特别是基于深度学习的模型，与物理分析模型中包含的领域特定知识相结合。该课程的重点是解决病态逆问题，这些问题是自然科学、医学和生命科学以及工程和工业应用中许多具有挑战性应用的核心。这篇调查论文的目的是给出一些在数据驱动的逆问题的主要贡献的说明。
Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data-driven models, and in particular those based on deep learning, with domain-specific knowledge contained in physical-analytical models. The focus is on solving ill-posed inverse problems that are at the core of many challenging applications in the natural sciences, medicine and life sciences, as well as in engineering and industrial applications. This survey paper aims to give an account of some of the main contributions in data-driven inverse problems.