Tootfinder

Learning Arbitrary-Scale RAW Image Downscaling with Wavelet-based Recurrent Reconstruction
Yang Ren, Hai Jiang, Wei Li, Menglong Yang, Heng Zhang, Zehua Sheng, Qingsheng Ye, Shuaicheng Liu
https://arxiv.org/abs/2507.23219

Learning Arbitrary-Scale RAW Image Downscaling with Wavelet-based Recurrent Reconstruction
Image downscaling is critical for efficient storage and transmission of high-resolution (HR) images. Existing learning-based methods focus on performing downscaling within the sRGB domain, which typically suffers from blurred details and unexpected artifacts. RAW images, with their unprocessed photonic information, offer greater flexibility but lack specialized downscaling frameworks. In this paper, we propose a wavelet-based recurrent reconstruction framework that leverages the information los…

EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules
Maybritt Schillinger, Maxim Samarin, Xinwei Shen, Reto Knutti, Nicolai Meinshausen
https://arxiv.org/abs/2509.26258

EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules
The practical use of future climate projections from global circulation models (GCMs) is often limited by their coarse spatial resolution, requiring downscaling to generate high-resolution data. Regional climate models (RCMs) provide this refinement, but are computationally expensive. To address this issue, machine learning models can learn the downscaling function, mapping coarse GCM outputs to high-resolution fields. Among these, generative approaches aim to capture the full conditional distr…

Generative multi-fidelity modeling and downscaling via spatial autoregressive transport maps
Alejandro Calle-Saldarriaga, Paul F. V. Wiemann, Matthias Katzfuss
https://arxiv.org/abs/2509.22474

Generative multi-fidelity modeling and downscaling via spatial autoregressive transport maps
Spatial fields are often available at multiple fidelities or resolutions, where high-fidelity data is typically more costly to obtain than low-fidelity data. Statistical surrogates or emulators can predict high-fidelity fields from cheap low-fidelity output. We propose a highly scalable Bayesian approach that can learn the joint non-Gaussian distribution and nonlinear dependence structure of nonstationary spatial fields at multiple fidelities from a small number of training samples. Our method …

Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature
Nils Bochow, Philipp Hess, Alexander Robinson
https://arxiv.org/abs/2507.22485

Physics-constrained generative machine learning-based high-resolution downscaling of Greenland's surface mass balance and surface temperature
Accurate, high-resolution projections of the Greenland ice sheet's surface mass balance (SMB) and surface temperature are essential for understanding future sea-level rise, yet current approaches are either computationally demanding or limited to coarse spatial scales. Here, we introduce a novel physics-constrained generative modeling framework based on a consistency model (CM) to downscale low-resolution SMB and surface temperature fields by a factor of up to 32 (from 160 km to 5 km grid spaci…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/7]:
- Domain-aligned generative downscaling enhances projections of extreme climate events
Ruian Tie, Xiaohui Zhong, Zhengyu Shi, Hao Li, Jun Liu, Wu Libo

Generative AI-Downscaling of Large Ensembles Project Unprecedented Future Droughts
Hamish Lewis, Neelesh Rampal, Peter B. Gibson, Luke J. Harrington, Chiara M. Holgate, Anna Ukkola, Nicola M. Maher
https://arxiv.org/abs/2509.21844

Generative AI-Downscaling of Large Ensembles Project Unprecedented Future Droughts
Understanding how droughts may change in the future is essential for anticipating and mitigating their adverse impacts. However, robust climate projections require large amounts of high-resolution climate simulations, particularly for assessing extreme events. Here, we use a novel dataset, multiple large-ensembles of Global Climate Models (GCMs), downscaled to 12km using generative AI, to quantify the future risk of meteorological drought across New Zealand. The ensembles consists of 20 GCMs, i…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[6/7]:
- Domain-aligned generative downscaling enhances projections of extreme climate events
Ruian Tie, Xiaohui Zhong, Zhengyu Shi, Hao Li, Jun Liu, Wu Libo

Domain-aligned generative downscaling enhances projections of extreme climate events
Ruian Tie, Xiaohui Zhong, Zhengyu Shi, Hao Li, Jun Liu, Wu Libo
https://arxiv.org/abs/2508.16396

Domain-aligned generative downscaling enhances projections of extreme climate events
Climate change is exacerbating extreme weather events globally, including high temperatures, extreme precipitation, strong winds, and tropical cyclones, posing severe threats to human health, infrastructure, food security, and socio-economic systems. Although existing global climate models (GCMs) provide essential tools for climate prediction, they face limitations such as insufficient resolution and high computational costs when simulating extreme events. To address these issues, this study pr…

Tunable magnon emission from a nano-optomagnet
Anna Duvakina, Vage Karakhanyan, Mingran Xu, Miguel-Angel Suarez, Axel J. M. Deenen, Marina Raschetti, Andrea Mucchietto, Thierry Grosjean, Dirk Grundler
https://arxiv.org/abs/2507.10742

Tunable magnon emission from a nano-optomagnet
The growing demand for dense, energy-efficient, and high-frequency signal processing continues to drive device miniaturization. While downscaling remains a central challenge, magnons offer a promising solution as nanoscale signal carriers, supporting broadband operation from GHz to THz without moving charge carriers and generating Joule heating. However, their integration at the nanoscale is limited by conventional electrical excitation based on coplanar waveguides, which require metal pads few…

Blind Super Resolution with Reference Images and Implicit Degradation Representation
Huu-Phu Do, Po-Chih Hu, Hao-Chien Hsueh, Che-Kai Liu, Vu-Hoang Tran, Ching-Chun Huang
https://arxiv.org/abs/2507.13915

Blind Super Resolution with Reference Images and Implicit Degradation Representation
Previous studies in blind super-resolution (BSR) have primarily concentrated on estimating degradation kernels directly from low-resolution (LR) inputs to enhance super-resolution. However, these degradation kernels, which model the transition from a high-resolution (HR) image to its LR version, should account for not only the degradation process but also the downscaling factor. Applying the same degradation kernel across varying super-resolution scales may be impractical. Our research acknowle…

On reflection, I think the big mistake is the conflation of #AI with #LLM and #MachineLearning.
There are genuine exciting advances in ML with applications all over the place, in science, (not least in my own research group looking at high resolution regional climate downscaling), health diagnostics, defence etc. But these are not the AIs that journalists are talking about, nor that are really related the LLMs.
They're still good uses of GPUs and will probably produce economic benefits, but probably not the multi- trillion ones the pundits seem to be expecting
#AIbubble is that I agree with so much of it.
I'm now wondering if I've missed something about #LLMs? The numbers and implications for stock markets are terrifyingly huge!
https://www.wheresyoured.at/the-haters-gui/

Typhoon Tracks Regulated by Feedbacks of Fine-Scale Clouds to Environment
Haoran Zhao, Shaoqing Zhang, Yang Gao, Lixin Wu, Yihan Cao, Wenju Cai, Bin Wang, L. Ruby Leung, Zebin Lu, Zhong Zhong, Xiaolin Yu, Mingkui Li, Chenyu Zhu
https://arxiv.org/abs/2509.26257

Typhoon Tracks Regulated by Feedbacks of Fine-Scale Clouds to Environment
Accurate tropical cyclone (TC) track prediction is crucial for mitigating the catastrophic impacts of TCs on human life and the environment. Despite decades of research on tropical cyclone (TC) track prediction, large errors known as track forecast busts (TFBs) occur frequently, and their causes remain poorly understood. Here, we examine a few dozens of TCs using a unique TC downscaling strategy that can quantitatively assess the sensitivity of TC track on the strength of feedbacks of fine-scal…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/4]:
- Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion
Yuhao Liu, James Doss-Gollin, Qiushi Dai, Ashok Veeraraghavan, Guha Balakrishnan

Down-scale marine hydrodynamic analysis at the Norwegian coast -- the NORA-SARAH open framework
Widar Weizhi Wang, Konstantinos Christakos, Csaba Pakozdi, Hans Bihs
https://arxiv.org/abs/2509.21329

Down-scale marine hydrodynamic analysis at the Norwegian coast -- the NORA-SARAH open framework
Offshore wave studies often assume Gaussian processes and homogeneous wave fields. However, as waves approach the shoreline, complex coastal topo-bathymetry induces transformations such as shoaling, refraction, diffraction, reflection, and breaking, leading to increased nonlinearity and site-specific wave characteristics. This complexity necessitates detailed site-specific studies for coastal infrastructure design and blue economy planning. This work presents a downscaling procedure for analyzi…

Intelligent Shanghai Typhoon Model (ISTM): A generative probabilistic emulator for typhoon hybrid modeling
Zeyi Niu, Wei Huang, Sirong Huang, Bo Qin, Mengqi Yang, Haofei Sun, Zhaoyang Huo, Haixia Xiao
https://arxiv.org/abs/2508.16851

Intelligent Shanghai Typhoon Model (ISTM): A generative probabilistic emulator for typhoon hybrid modeling
To address the systematic underestimation of typhoon intensity in artificial intelligence weather prediction (AIWP) models, we propose the Intelligent Shanghai Typhoon Model (ISTM): a unified regional-to-typhoon generative probabilistic forecasting system based on a two-stage UNet-Diffusion framework. ISTM learns a downscaling mapping from 4 years of 25 km ERA5 reanalysis to a 9 km high resolution typhoon reanalysis dataset, enabling the generation of kilometer-scale near-surface variables and …

Replaced article(s) found for physics.ao-ph. https://arxiv.org/list/physics.ao-ph/new
[1/1]:
- Are Deep Learning Methods Suitable for Downscaling Global Climate Projections? An Intercomparison...
Jose Gonz\'alez-Abad, Jos\'e Manuel Guti\'errez