Solving inverse problems using data-driven models

被引：326

作者：

Arridge, Simon ^{[1
]}

Maass, Peter ^{[2
]}

Oktem, Ozan ^{[3
]}

Schonlieb, Carola-Bibiane ^{[4
]}

机构：

[1] UCL, Dept Comp Sci, Gower St, London WC1E 6BT, England

[2] Univ Bremen, Dept Math, Postfach 330 440, D-28344 Bremen, Germany

[3] KTH Royal Inst Technol, Dept Math, SE-10044 Stockholm, Sweden

[4] Univ Cambridge, Dept Appl Math & Theoret Phys, Wilberforce Rd, Cambridge CB3 0WA, England

来源：

ACTA NUMERICA | 2019年 / 28卷

基金：

英国工程与自然科学研究理事会;

关键词：

LOW-DOSE CT; CONVOLUTIONAL NEURAL-NETWORKS; ILL-POSED PROBLEMS; GENERATIVE ADVERSARIAL NETWORK; POSTERIOR CONTRACTION RATES; IMAGE-RESTORATION; CONVERGENCE-RATES; SIGNAL RECOVERY; REGULARIZATION METHODS; TIKHONOV REGULARIZATION;

D O I：

10.1017/S0962492919000059

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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.

引用

页码：1 / 174

页数：174

共 50 条

[31] Data-driven inverse optimization with imperfect information
Peyman Mohajerin Esfahani
Soroosh Shafieezadeh-Abadeh
Grani A. Hanasusanto
Daniel Kuhn
[J]. Mathematical Programming, 2018, 167 : 191 - 234
[32] DATA-DRIVEN INVERSE DESIGN METHOD FOR TURBOMACHINERY
So, Kwok Kai
Salamanca, Luis
Ozdemir, Firat
Perez-Cruz, Fernando
[J]. PROCEEDINGS OF ASME TURBO EXPO 2024: TURBOMACHINERY TECHNICAL CONFERENCE AND EXPOSITION, GT2024, VOL 12D, 2024,
[33] Topology Aware Data-Driven Inverse Kinematics
Ho, Edmond S. L.
Shum, Hubert P. H.
Cheung, Yiu-ming
Yuen, P. C.
[J]. COMPUTER GRAPHICS FORUM, 2013, 32 (07) : 61 - 70
[34] A Data-Driven Approach for Inverse Optimal Control
Liang, Zihao
Hao, Wenjian
Mou, Shaoshuai
[J]. 2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC, 2023, : 3632 - 3637
[35] Data-driven algorithms for inverse design of polymers
Sattari, Kianoosh
Xie, Yunchao
Lin, Jian
[J]. SOFT MATTER, 2021, 17 (33) : 7607 - 7622
[36] Data-driven Inverse Dynamics for Human Motion
Lv, Xiaolei
Chai, Jinxiang
Xia, Shihong
[J]. ACM TRANSACTIONS ON GRAPHICS, 2016, 35 (06):
[37] Data-driven linearizing approach in inverse scattering
Marengo, Edwin A.
Galagarza, Edson S.
Solimene, Raffaele
[J]. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2017, 34 (09) : 1561 - 1576
[38] Data-driven inverse optimization with imperfect information
Esfahani, Peyman Mohajerin
Shafieezadeh-Abadeh, Soroosh
Hanasusanto, Grani A.
Kuhn, Daniel
[J]. MATHEMATICAL PROGRAMMING, 2018, 167 (01) : 191 - 234
[39] Parameterizing Variational Methods Through Data-Driven Inverse Problems for Image Processing Applications
Santos, Italo M. F.
Giraldi, Gilson A.
Blanco, Pablo Javier
Loula, Abimael D.
[J]. PROCEEDINGS OF THE 2020 INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP), 27TH EDITION, 2020, : 211 - 216
[40] A data-driven paradigm for mapping problems
Zhang, Peng
Liu, Ling
Deng, Yuefan
[J]. PARALLEL COMPUTING, 2015, 48 : 108 - 124

← 1 2 3 4 5 →