Data-driven generative digital twins for wind-farm flows from highly sparse measurements

Abstract

Reconstructing high-fidelity flow fields from sparse measurements remains a central challenge in applied sensing and real-time flow control. This work addresses the problem in a real-world setting, focusing on turbulent wind-farm flows. To span a range of reconstruction paradigms, we compare three approaches: gappy proper orthogonal decomposition (GPOD), a linear reduced-order baseline; the shallow recurrent decoder (SHRED), a state-of-the-art deep learning method; and a proposed observation-guided generative framework (GGenAI). The methods are systematically evaluated on a wind-farm dataset across sensor densities ranging from 0.12% to 6.65% of the spatial domain. The results reveal a clear performance hierarchy. In the extremely sparse regime ( < 0.49% coverage), GPOD yields relatively low global reconstruction error; however, qualitative analysis indicates that this performance reflects recovery of the mean field rather than instantaneous dynamics. In this same regime, SHRED exhibits behavior similar to GPOD. As sensor coverage increases (around 0.49%), SHRED begins to recover meaningful coarse flow structures. However, the reconstructions remain overly smooth and fail to capture instantaneous turbulent features. In contrast, GGenAI undergoes an earlier transition toward instantaneous-like reconstruction, and at higher sensor densities (beyond approximately 1% coverage) it consistently outperforms both baselines. To further assess physical consistency, we introduce a POD-based reduced-order reference field representing the dominant energy-containing flow structures. GGenAI exhibits improved agreement with this reduced-order reference, emphasizing that reconstruction quality must be interpreted in relation to relevant spatial scales. Overall, the study demonstrates that our GGenAI approach extends to complex turbulent flows, offering a promising pathway toward data-driven digital twins.