MS026 - Residual Minimization Methods for Flow Modelling and Simulation
Keywords: Automatic adaptivity, automatic discrete stability, Deep Learning Discretizations, Discontinuous Petrov-Galerkin (DPG) Method
, Least-Squares Finite Elements
, numerical stabilization of discretisations, Residual Minimization
The 23rd IACM Computational Fluids Conference (CFC 2025) is an excellent opportunity to bring together leading researchers and professionals in residual minimization methods and their application to computational fluid dynamics (CFD) and industrial applications. This workshop will focus on Residual Minimization Methods, including the Discontinuous Petrov-Galerkin (DPG) method and Least-Squares Finite Elements. It will also cover deep learning-based discretisations of PDEs using residual minimization loss functions to improve accuracy and make training more robust. The minisymposium will explore how these advanced mathematical techniques contribute to more accurate and stable numerical solutions. Residual minimization techniques can improve CFD simulations' stability, accuracy, and convergence. We expect a thorough discussion of their benefits in addressing common numerical challenges, such as ill-conditioning, boundary and internal layer resolution, and solution stabilization. These sessions will discuss applications in fluid dynamics, heat transfer, multi-phase flows, turbulence modelling, and industrial process optimization.