3 papers
physics.flu-dyn2025
Accelerating high-order energy-stable discontinous Galerkin solver using auto-differentiation and neural networks
Xukun Wang, Oscar A. Marino, Esteban Ferrer
High-order Discontinuous Galerkin Spectral Element Methods (DGSEM) provide excellent accuracy for complex flow simulations, but their computational cost increases sharply with high…
physics.flu-dyn2025
A novel parallelizable convergence accelerating method: Pointwise Frequency Damping
Zikun Liu, Xukun Wang, Yilang Liu +1
This paper proposes a novel class of data-driven acceleration methods for steady-state flow field solvers. The core innovation lies in predicting and assigning the asymptotic limit…
physics.flu-dyn2025
A data-driven convergence booster for accelerating and stabilizing pseudo time-stepping
Xukun Wang, Yilang Liu, Xiang Yang +1
This paper introduces a novel data-driven convergence booster that not only accelerates convergence but also stabilizes solutions in cases where obtaining a steady-state solution i…