PDESoft 2026

PDE software frameworks conference 2026

will take place 13 – 15 July 2026 at TU Wien, Austria

The numerical solution of partial differential equations (PDEs) is important across a wide range of fields. Simulation codes underpin this need and are employed in many areas of science and engineering, from forecasting the weather, to predicting the flow field around aircraft, through to predictive simulations for biomedical applications, amongst many other examples.

PDESoft 2026 is an opportunity for users and developers of software tools for solving PDEs and allied areas to come together to present and discuss current and future directions of research.

Areas of focus include:

  • numerical PDE solvers
  • new and novel software tools for solving PDEs, including machine learning techniques
  • parallel and high-performance computing, including at exascale
  • algorithmic and software techniques for novel hardware
  • numerical linear algebra and preconditioning
  • model order reduction
  • meshing tools
  • data visualisation
  • user interfaces to scientific software
  • any other part of the PDE toolchain

PDESoft 2026 follows in the successful footsteps of PDESoft 2012 in Münster, PDESoft 2014 in Heidelberg, PDESoft 2016 in Warwick, PDESoft 2018 in Bergen and PDESoft 2024 in Cambridge.

Scientific committee

  • Jed Brown, University of Colorado
  • Donna Calhoun, Boise State University
  • Christian Engwer, Universität Münster
  • David Ham, Imperial College London
  • Timo Heister, Clemson University
  • Robert Klöfkorn, Lunds Universitet
  • Tzanio Kolev, Lawrence Livermore National Laboratory
  • Martin Kronbichler, Universität Augsburg
  • Sylvain Laizet, Imperial College London
  • Marie Rognes, Simula Oslo
  • Miriam Schulte, Universität Stuttgart
  • Garth Wells, University of Cambridge
  • Michèle Weiland, Edinburgh Parallel Computing Centre
  • Ulrike Yang, Lawrence Livermore National Laboratory

Local organizing committee

  • Joachim Schöberl, Institute of Analysis and Scientific Computing, TU Wien
  • Markus Wess, Institute of Analysis and Scientific Computing, TU Wien