Conditional flow matching for generative modeling of near-wall turbulence with quantified uncertainty

Reconstructing near-wall turbulence from wall-based measurements is a critical yet inherently ill-posed problem in wall-bounded flows, where limited sensing and spatially heterogeneous flow-wall coupling challenge deterministic estimation strategies. To address this, we introduce a novel generative modeling framework based on conditional flow matching for synthesizing instantaneous velocity fluctuation fields from wall observations, with explicit quantification of predictive uncertainty. Our method integrates continuous-time flow matching with a probabilistic forward operator trained using stochastic weight averaging Gaussian (SWAG), enabling zero-shot conditional generation without model retraining. We demonstrate that the proposed approach not only recovers physically realistic, statistically consistent turbulence structures across the near-wall region but also effectively adapts to various sensor configurations, including sparse, incomplete, and low-resolution wall measurements. The model achieves robust uncertainty-aware reconstruction, preserving flow intermittency and structure even under significantly degraded observability. Compared to classical linear stochastic estimation (LSE) and deterministic convolutional neural networks (CNN) methods, our stochastic generative learning framework exhibits superior generalization and resilience under measurement sparsity with quantified uncertainty. This work establishes a robust semi-supervised generative modeling paradigm for data-consistent flow reconstruction and lays the foundation for uncertainty-aware, sensor-driven modeling of wall-bounded turbulence.