Guest Editors: 
Francesco Piccialli, francesco.piccialli@unina.it
Salvatore Cuomo, salvatore.cuomo@unina.it
Boumediene Hamzi, b.hamzi@imperial.ac.uk
Jan Hesthaven, Jan.Hesthaven@epfl.ch

Submission deadline: December 31, 2021
 

We are pleased to solicit submissions to the Topical Collection "Beyond traditional AI: the impact of Machine Learning on Scientific Computing".
In order to add another piece to a complicated but fascinating puzzle, this topical collection aims to attract high-quality contributions to investigate both the role of ML/DL methodologies in applied mathematics and how Scientific Computing can benefit from learning paradigms.

Guest Editors: Yanlai Chen, yanlai.chen@umassd.edu; Sigal Gottlieb, sigal_gottlieb@icerm.brown.edu; Serkan Gugercin, gugercin@vt.edu; Misha Kilmer, misha.kilmer@tufts.edu; Akil Narayan, akil@sci.utah.edu; and Daniele Venturi, venturi@ucsc.edu

Submission deadline: December 1, 2020

We are pleased to solicit submissions to the Topical Collection of JSC dedicated to the ICERM Spring 2020 program on "Model Order Reduction. Aiming to focus research effort on current areas of promising research and to galvanize new and existing collaborations, the Spring 2020 ICERM semester program focused on both theoretical investigation and practical algorithm development for reduction in the complexity - the dimension, the degrees of freedom, the data - arising in these models. The program in particular aimed to integrate diverse fields of mathematical analysis, statistical sciences, data and computer science, and specifically to attract researchers working in the areas of model order reduction, data-driven model calibration and simplification, computational approximation in high dimensions, and data-intensive uncertainty quantification. The four broad thrusts of the program are (1) mathematics of reduced order models, (2) algorithms for approximation and complexity reduction, (3) computational statistics and data-driven techniques, and (4) application-specific design.

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.