DD25 Poster Session Information (Organizer: Alex Bihlo)

We hope to have enough interest to have a poster session at DD25. Please send an email to dd25@mun.ca with your name, institution, poster title and abstract. We will get back to you with details.

The current list of accepted posters and their proposers are included below

Accepted Posters

  • Faycal Chaouqui: On the scalability of classical one-level domain-decomposition methods
  • Chen Greif: SPMR: a Family of Saddle-Point Minimum Residual Solvers
  • Dawei Wang: A monotonicity-preserving multigrid algorithm for solving the equidistributing meshes in 1D
  • M.J. Gander and T. Lunet: A Reynolds Number Dependent Convergence Estimate for the Parareal Algorithm
  • Lukas Maly: Domain Decomposition of Mixed Finite Element Method in ESPRESO
  • V. Agoshkov: New approach to formulation of domain decomposition method in the problems of oceans and seas hydrothermodynamics
  • Natalia Lezina: Domain decomposition method for the Baltic Sea model based on theory of inverse problems and adjoint equation
  • Martin J. Gander, Tommaso Vanzan: Multilevel Optimized Schwarz Methods
  • Pratik Nayak, Hartwig Anzt: A generic framework for Schwarz decomposition methods.
  • Alyson Fox: Stability Analysis of Inline ZFP Compression for Floating-Point Data in Iterative Methods
  • Terry Haut, Peter Maginot, Ben Southworth, Vladimir Tomov: Solving High-order Discretizations of Thermal Radiative Transport
  • Serge Van Criekingen, Martin Gander: New Coarse Corrections for Optimized Restricted Additive Schwarz Using PETSc.
  • Soheil Hajian: A new approach for preconditioning discontinuous Galerkin discretizations.
  • Toktam Zand, Ali Gholami, Alison Malcolm: Consensus Least-squares Reverse Time Migration
  • Fabrizio Donzelli: Convergence of classical Schwarz method for the 2-dimensional Maxwell's equations


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We also acknowledge the support of the Department of Mathematics and Statistics and Faculty of Engineering at Memorial and the United States national labs LANL (Los Alamos National Lab) and LLNL (Lawrence Livermore National Lab)