Advances in Inverse Problems and Machine Learning Solutions
A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Logic".
Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 885
Special Issue Editor
Special Issue Information
Dear Colleagues,
Inverse problems are a type of problem where the objective is to identify the underlying characteristics that give rise to the observed or measured data. Machine learning has emerged as a powerful tool for solving inverse problems. Recent advances in solving inverse problems with machine learning have led to new solutions for understanding physical and socio-economic systems, where a critical problem is to infer physical parameters from observations.
This Special Issue of Axioms aims to address the recent approaches and solutions of machine learning for inverse problems, as well as their practical applications. Original research articles and reviews are welcome, with research areas including:
- Regularization methods for inverse problems;
- Bayesian inference for inverse problems;
- Deep learning for inverse problems;
- Inverse problems in physics;
- Inverse problems in lattice calculations;
- Inverse problems in medical imaging;
- Inverse problems in socio-economic systems;
- Uncertainty quantification in inverse problems.
Contributions to this Special Issue will help to advance our understanding of the inverse problem in different scientific domains. We look forward to receiving your contributions
Dr. Lingxiao Wang
Guest Editor
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Axioms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- inverse problem
- machine learning
- Bayesian inference
- Bayesian inference for inverse problems
- deep learning for inverse problems
- inverse problems in physics
- inverse problems in lattice calculations
- inverse problems in medical imaging
- inverse problems in socio-economic systems
- uncertainty quantification in inverse problems
