NAFeb 17, 2017
Numerical Analysis of the Leray Reduced Order ModelXuping Xie, David Wells, Zhu Wang et al.
Standard ROMs generally yield spurious numerical oscillations in the simulation of convection-dominated flows. Regularized ROMs use explicit ROM spatial filtering to decrease these spurious numerical oscillations. The Leray ROM is a recently introduced regularized ROM that utilizes explicit ROM spatial filtering of the convective term in the Navier-Stokes equations. This paper presents the numerical analysis of the finite element discretization of the Leray ROM. Error estimates for the ROM differential filter, which is the explicit ROM spatial filter used in the Leray ROM, are proved. These ROM filtering error estimates are then used to prove error estimates for the Leray ROM. Finally, both the ROM filtering error estimates and the Leray ROM error estimates are numerically investigated in the simulation of the two-dimensional Navier-Stokes equations with an analytic solution.
CVSep 7, 2025
OmniStyle2: Scalable and High Quality Artistic Style Transfer Data Generation via DestylizationYe Wang, Zili Yi, Yibo Zhang et al.
OmniStyle2 introduces a novel approach to artistic style transfer by reframing it as a data problem. Our key insight is destylization, reversing style transfer by removing stylistic elements from artworks to recover natural, style-free counterparts. This yields DST-100K, a large-scale dataset that provides authentic supervision signals by aligning real artistic styles with their underlying content. To build DST-100K, we develop (1) DST, a text-guided destylization model that reconstructs stylefree content, and (2) DST-Filter, a multi-stage evaluation model that employs Chain-of-Thought reasoning to automatically discard low-quality pairs while ensuring content fidelity and style accuracy. Leveraging DST-100K, we train OmniStyle2, a simple feed-forward model based on FLUX.1-dev. Despite its simplicity, OmniStyle2 consistently surpasses state-of-the-art methods across both qualitative and quantitative benchmarks. Our results demonstrate that scalable data generation via destylization provides a reliable supervision paradigm, overcoming the fundamental challenge posed by the lack of ground-truth data in artistic style transfer.