Journal updates

  • Transmission Eigenvalues and Related Spectral Problems in Scattering Theory (Submission Deadline: April 30, 2021)

    This special issue will feature recent developments in the theory and applications of transmission eigenvalues and related spectral problems in direct and inverse scattering theory. The transmission eigenvalue problem is at the heart of inverse scattering theory for inhomogeneous media. It has a deceptively simple formulation but presents a perplexing mathematical structure; in particular it is a non-self-adjoint eigenvalue problem. This subject is rich, active and in the past decade has taken a multitude of directions, including developments in the spectral theory for various operators related to scattering, as well as many applications in inverse scattering problems and imaging. We solicit high quality original research papers targeting results on the theory, computations and applications of these topics.

    Guest Editor: Fioralba Cakoni, Rutgers University and Houssem Haddar, CMAP Ecole Polytechnique

    Submission Deadline: April 30, 2021

    Download full details here: Transmission eigenvalues and Related Spectral Problems in Scattering Theory (PDF, 19.17 kB)

  • 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, Thomas Hou, Jinchao Xu
    Submission Deadline: August 31, 2021
    Download full details here: Mathematical Theory of Machine Learning and Applications

  • PDE Methods for Machine Learning (Submission Deadline: 31st August 2021)

    This special issue will feature recent developments in the application of partial differential equations (PDE) to problems in machine learning. We solicit high quality original research papers targeting the analysis and applications of PDEs to problems in machine learning and data science.

    Guest Editors: Jeff Calder (University of Minnesota), Xiuyuan Cheng (Duke University), Adam Oberman (McGill University), Lars Ruthotto (Rutgers University)

    Submission Deadline: 31st August 2021

    Download Full Details Here: 

    PDE Methods for Machine Learning

  • Developments in Commutative Algebra: In honor of Jürgen Herzog on the occasion of his 80thBirthday

    Jurgen Herzog is one of the most accomplished researchers in the modern developments of commutative algebra. He has produced more than 230 original research papers and is cited more than 6650 times by approximately 2150 authors. In honor of his great achievements, we look forward to publishing a special issue commemorating his 80th birthday and honoring his influence on the field of commutative algebra and mathematics in general.

    Guest Editor: Takayuki Hibi 
    Submission Deadline: 31st December 2021
    Download Full Details Here: Developments in Commutative Algebra

  • Best Paper Award

    Initiated in 2019 in celebration of its 5th Anniversary, Research in the Mathematical Sciences’ Best Article Award recognizes outstanding and highly influential research published in the journal.

  • COVID-19 and impact on peer review

    As a result of the significant disruption that is being caused by the COVID-19 pandemic we are very aware that many researchers will have difficulty in meeting the timelines associated with our peer review process during normal times.  Please do let us know if you need additional time. Our systems will continue to remind you of the original timelines but we intend to be highly flexible at this time.