Collection
Mathematical Theory of Machine Learning and Applications
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 features high-quality original research relating to the theoretical and computational developments in these topics.
Editors
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John Harlim
Institute for Computational and Data Sciences, The Pennsylvania State University
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Thomas Hou
California Institute of Technology
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Jinchao Xu
The Pennsylvania State University
