Stochastic Super Resolution For Gaussian Textures Paper And Code Although only demonstrated for a 2d case study, these results highlight the potential of using stochastic interpolants to super resolve turbulent flows. These results establish stochastic interpolants as a viable tool for super resolving turbulent flows and highlight their potential for future applications.
Stochastic Super Resolution Of Cosmological Simulations With Denoising Designed to enhance low resolution simulations, les, or experimental data, the approach can be applied either to reconstruct the full field in a single pass or to super resolve smaller patches, enabling iterative recovery of the full domain or targeted regions of interest. Conducting wind field super resolution (sr) reconstruction using limited dataset is crucial for analyzing wind effects on wind energy equipment and optimizing wind energy utilization. In recent years, deep learning models have been successfully employed for augmenting low resolution cosmological simulations with small scale information, a task known as “super resolution”. These results establish stochastic interpolants as a viable tool for super resolving turbulent flows and highlight their potential for future applications.
Stochastic Super Resolution For Downscaling Time Evolving Atmospheric In recent years, deep learning models have been successfully employed for augmenting low resolution cosmological simulations with small scale information, a task known as “super resolution”. These results establish stochastic interpolants as a viable tool for super resolving turbulent flows and highlight their potential for future applications. Although only demonstrated for a 2d case study, these results highlight the potential of using stochastic interpolants to super resolve turbulent flows. In this paper, we propose a novel uncertainty driven framework for stochastic sisr that incorporates anisotropic gaussian priors modulated by estimated uncertainty maps. In recent years, deep learning models have been successfully employed for augmenting low resolution cosmological simulations with small scale information, a task known as "super resolution".
Face Super Resolution Using Stochastic Differential Equations Although only demonstrated for a 2d case study, these results highlight the potential of using stochastic interpolants to super resolve turbulent flows. In this paper, we propose a novel uncertainty driven framework for stochastic sisr that incorporates anisotropic gaussian priors modulated by estimated uncertainty maps. In recent years, deep learning models have been successfully employed for augmenting low resolution cosmological simulations with small scale information, a task known as "super resolution".
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