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Journal of Scientific Computing - Topical Collection dedicated to the ICERM Spring 2020 semester program on Model Order Reduction

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
  • Daniele Venturi, venturi@ucsc.edu

Modern computational and experimental paradigms have complex models along with variegated and frequently enormous data sets. This necessitates development of numerical algorithms whose theoretical and algorithmic underpinnings guarantee robust resolution of important features and characteristics in complex computational models. The desiderata for resolving the underlying model features is often application-specific and combines mathematical tasks like approximation, prediction, calibration, design, and optimization. Simulations that fully account for variability of complexities in modern scientific models is frequently infeasible due to overwhelming model cost exacerbated by the curse of dimensionality, chaotic behavior or dynamics, or overwhelming streams of data. The overarching goal of model order reduction is to battle these challenges by building intelligently simplified computational models that are both more tractable than, and nearly as accurate as, the corresponding full model.

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.

With a successful program resulting in substantial progress, the primary goal of this special issue is to introduce to the JSC readership recent results and new directions in model order reduction, particularly those performed during or inspired by the semester program activities and partnerships forged therein. The semester program enjoyed support from the National Science Foundation through Grant No. DMS-1439786 and from the Simons Foundation through Grant No. 50736.

Submission deadline: December 1st, 2020

Submission: Manuscripts should be submitted electronically to https://www.editorialmanager.com/jomp/default.aspx (this opens in a new tab).

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