Trends and Prospects in Mathematical and Scientific Machine Learning
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".
Deadline for manuscript submissions: 21 November 2024 | Viewed by 109
Special Issue Editor
Special Issue Information
Dear Colleagues,
This Special Issue invites research contributions in the realm of mathematical and scientific machine learning. We specifically encourage investigations focused on the development and mathematical analysis of machine learning or data-driven models aimed at addressing critical scientific computing challenges. These challenges include solving partial/ordinary differential equations, deriving model reductions, constructing neural operators, learning governing equations, exploring the downscaling of reduced-order models, delving into multioperator learning, and designing random feature neural networks. Submissions dealing with very new topics related to large language models and scientific machine learning are also warmly welcomed.
Contributions from diverse fields such as physics, chemistry, biology, and engineering are welcome, with a particular interest in multiscale applications and those involving heterogeneous properties. We are keen to present research that introduces algorithms and analyzes universal approximation and convergence. However, contributions involving pure analysis of network convergence or pure algorithm or network designs are also highly encouraged.
Dr. Zecheng Zhang
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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
- neural operators
- random feature neural network
- physics-informed learning
- data-driven model reduction
- multioperator learning
- neural network convergence analysis
- universal approximation of neural network
- large language model and scientific machine learning
