Composite Classifier-Free Guidance for Multi-Modal Conditioning in Wind Dynamics Super-Resolution

• URL: http://arxiv.org/abs/2512.13729v1
• Date: Sat, 13 Dec 2025 22:44:41 GMT
• Title: Composite Classifier-Free Guidance for Multi-Modal Conditioning in Wind Dynamics Super-Resolution
• Authors: Jacob Schnell, Aditya Makkar, Gunadi Gani, Aniket Srinivasan Ashok, Darren Lo, Mike Optis, Alexander Wong, Yuhao Chen,
• Abstract summary: Wind data is distinct from natural images.<n>Wind super-resolvers often use upwards of 10 input channels, significantly more than the usual 3-channel RGB inputs in natural images.<n>We present WindDM, a diffusion model trained for industrial-scale wind dynamics reconstruction.
• Score: 45.85259700126175
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Various weather modelling problems (e.g., weather forecasting, optimizing turbine placements, etc.) require ample access to high-resolution, highly accurate wind data. Acquiring such high-resolution wind data, however, remains a challenging and expensive endeavour. Traditional reconstruction approaches are typically either cost-effective or accurate, but not both. Deep learning methods, including diffusion models, have been proposed to resolve this trade-off by leveraging advances in natural image super-resolution. Wind data, however, is distinct from natural images, and wind super-resolvers often use upwards of 10 input channels, significantly more than the usual 3-channel RGB inputs in natural images. To better leverage a large number of conditioning variables in diffusion models, we present a generalization of classifier-free guidance (CFG) to multiple conditioning inputs. Our novel composite classifier-free guidance (CCFG) can be dropped into any pre-trained diffusion model trained with standard CFG dropout. We demonstrate that CCFG outputs are higher-fidelity than those from CFG on wind super-resolution tasks. We present WindDM, a diffusion model trained for industrial-scale wind dynamics reconstruction and leveraging CCFG. WindDM achieves state-of-the-art reconstruction quality among deep learning models and costs up to $1000\times$ less than classical methods.

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