Mathematical Theory of Machine Learning and Applications (Submission Deadline: 31st August, 2021)

In the past decade, deep learning as a branch of machine learning has influenced scientific computing in a fundamental way. This computational breakthrough presents tremendous opportunities and needs for new perspectives on computational mathematics and related emerging fields, such as approximation theory, operator estimation, numerical PDEs, inverse problems, data-driven modeling of dynamical systems, unsupervised and semi-supervised learnings. This special issue will feature high-quality original research, including (but not limited to) the theoretical and computational developments in these topics.

Guest Editors:
John Harlim, Penn State University
email: jzh13@psu.edu

Thomas Hou, California Technical Institute
email: hou@cms.caltech.edu

Jinchao Xu, Penn State University
email: xu@math.psu.edu


Submission Deadline: August 31, 2021

Submission Procedure:

  • Papers should be submitted at the Research in the Mathematical Sciences website: www.editorialmanager.com/rmsb/default.aspx
  • Select Article Type: Manuscript
  • Upload your files: When the system asks, "Does this manuscript belong to a special issue?" reply: Yes, then choose the option " Mathematical Theory of Machine Learning and Applications "
  • Complete the submission process as required.

For all information and further details, please download the document: Mathematical Theory of Machine Learning and Applications