Recent Advances in Numerical Linear Algebra for PDEs, Optimization, and Data Assimilation

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Recent Advances in Numerical Linear Algebra for PDEs, Optimization, and Data Assimilation

 11 - 13 Apr 2022

ICMS, Bayes Centre, Edinburgh

 Enquiries

Scientific Organisers

  • John Pearson, University of Edinburgh
  • Jemima Tabeart, University of Edinburgh

About:

High-dimensional problems from numerical linear algebra underpin a wide variety of mathematical and scientific applications, including the solution of large-scale systems arising from PDEs, optimization problems, and data assimilation for weather forecasting. In this workshop the fast and robust solution of linear systems that result from such problems were considered. The presentations discussed the use of widely-applicable numerical linear algebra techniques as well as problem-specific tools, in order to design efficient implementations and algorithms to exploit modern computer architectures.

This workshop consisted of presentations from a number of leading researchers from academia and industry, aimed to encourage an exchange of ideas to further advance the state of the art in the application of numerical linear algebra to PDEs, optimization, and data assimilation.

Titles and Abstracts

Programme

Please find below the programme for the Recent Advances in Numerical Linear Algebra for PDEs, Optimization, and Data Assimilation workshop.

Playlist of approved talks
Monday 11 April
Introductions and Jemima Tabeart , University of Edinburgh
Coffee
Serge Gratton (Online Lecture), INP-ENSEEIHT, Toulouse Data Assimilation Recurrent networks can beat Ensemble methods
Patrick Farrell, University of Oxford Reynolds-robust preconditioners for the stationary incompressible Navier-Stokes equations
Lunch
Jennifer Scott, University of Reading/Rutherford Appleton Laboratory Can randomised preconditioning improve the weather forecast?
Ieva Dauzickaite, Charles University Randomised preconditioning for saddle point systems in data assimilation
Coffee
Aretha Teckentrup, University of Edinburgh Convergence and Robustness of Gaussian Process Regression
Stefano Cipolla, University of Edinburgh Primal Dual Regularized IPM: a Proximal Point perspective
Discussion (in-person only) Zoom closes for the day
Welcome reception in Bayes
Tuesday 12 April
Joanne Waller, Met Office Can we handle spatial observation error correlations in atmospheric data assimilation?
Coffee
Selime Gurol, CERFACS Latent Space Data Assimilation
Hussam al Daas, Rutherford Appleton Laboratory, STFC Recent advances in robust algebraic domain decomposition preconditioners
Lunch
Jonas Latz, Heriot-Watt University Fast and even faster sampling of parameterised Gaussian random fields
Filippo Zanetti, University of Edinburgh New indicators for the early termination of the linear solver in interior point methods
Coffee
Lingyi Yang, University of Oxford Path signatures and neural controlled differential equations for prediction tasks
Jonna Roden (Online Lecture), University of Edinburgh Spectral element methods and iterative solvers for PDE-constrained optimization problems
Wednesday 13 April
Alison Ramage, University of Strathclyde Preconditioning for Data Assimilation Problems
Coffee
Silvia Gazzola, University of Bath Regularization by inexact Krylov methods
Davide Palitta, Alma Mater Studiorum, Universita' di Bologna Stein-based Preconditioners for Weak-constraint 4D-var
Closing remarks There will be a take away lunch provided