A Fast Sine Transform Accelerated High-Order Finite Difference Method for Parabolic Problems over Irregular Domains
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- Chi Wang Shu
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Topical Collection: Beyond traditional AI: the impact of Machine Learning on Scientific Computing
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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 extended: February 28, 2022
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. -
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; 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.
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