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Works by Category

A list of papers, proceedings, and preprints by category.

Analysis

  1. Analysis and numerical analysis of the Helmholtz-Korteweg equation Patrick E. Farrell, Tim van Beeck, UZ arXiv, 2025.

Mathematical Physics and Modelling

  1. A Kinetic Theory Approach to Ordered Fluids José A. Carrillo, Patrick E. Farrell, Andrea Medaglia, UZ arXiv, 2025.
  2. Time-harmonic waves in Korteweg and nematic-Korteweg fluids Patrick E Farrell, UZ [PRE, arXiv], 2025.
  3. Kinetic Derivation of an Inviscid Compressible Leslie–Ericksen Equation for Rarified Calamitic Gases Patrick E Farrell, Giovanni Russo, UZ [SIAM MMS, arXiv], 2024.

Numerical Analysis

  1. A nodal ghost method based on variational formulation and regular square grid for elliptic problems on arbitrary domains in two space dimensions
    Clarissa Astuto, Daniele Boffi, Giovanni Russo, UZ [CMAME, arXiv], 2025.
  2. The Lightning Virtual Element Method for Self-adjoint Eigenvalue Problems
    Manuel Trezzi, UZ GIMC SIMAI Young 2024, 2025
  3. The High-Order Lightning Virtual Element Method
    Manuel Trezzi, UZ GIMC SIMAI Young 2024, 2025
  4. Preconditioned normal equations for solving discretised partial differential equations Lorenzo Lazzarino, Yuji Nakatsukasa, UZ arXiv, 2025.
  5. When rational functions meet virtual elements: the lightning virtual element method
    Manuel Trezzi, UZ [CALCOLO, arXiv, errata], 2024.
  6. A comparison of the Coco-Russo scheme and $\protect\mathghost$-FEM for elliptic equations in arbitrary domains
    Clarissa Astuto, Armando Coco, UZ arXiv, 2024.
  7. An adaptive mesh refinement strategy to ensure quasi-optimality of the conforming finite element method for the Helmholtz equation via T-coercivity
    Tim van Beeck, UZ arXiv, 2024.
  8. PINNs and GaLS: A Priori Error Estimates for Shallow Physics Informed Neural Networks Applied to Elliptic Problems UZ IFAC-PapersOnLine, 2022 [arXiv]

Scientific Computing

  1. ngsPETSc: A coupling between NETGEN/NGSolve and PETSc
    Jack Betteridge, Patrick E. Farrell, Matthias Hochsteger, Christopher Lackner, Joachim Schöberl, Stefano Zampini, UZ [JOSS, GitHub], 2024.
  2. PETScML: Second-Order Solvers for Training Regression Problems in Scientific Machine Learning
    Stefano Zampini, UZ, George Turkyyiah, David Elliot Keyes PASC’24, 2024 [arXiv]