PODiff performs conditional diffusion in a fixed, variance-ordered POD latent space to enable efficient probabilistic super-resolution of high-dimensional scientific fields with lower memory and better-calibrated uncertainty than pixel-space or dropout baselines.
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UNVERDICTED 3representative citing papers
The OWL-v2026 is a ~220 USD open-source logger that records six-axis IMU data at 208-416 Hz and GNSS data at 10 Hz with sub-10 ms UTC timestamp accuracy, validated for over 10 days of continuous operation at low power.
An adaptive spatiotemporal clustering framework boosts deep learning reconstruction of global ocean subsurface temperature fields from surface data, delivering 12.4% to 27.2% RMSE improvements when paired with models such as DP-CNN, Attention U-Net, and ViT.
citing papers explorer
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PODiff: Latent Diffusion in Proper Orthogonal Decomposition Space for Scientific Super-Resolution
PODiff performs conditional diffusion in a fixed, variance-ordered POD latent space to enable efficient probabilistic super-resolution of high-dimensional scientific fields with lower memory and better-calibrated uncertainty than pixel-space or dropout baselines.
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OpenWaveLogger v2026 (OWL-v2026): an open source, low cost, easy to build, high performance logger for wave data measurements
The OWL-v2026 is a ~220 USD open-source logger that records six-axis IMU data at 208-416 Hz and GNSS data at 10 Hz with sub-10 ms UTC timestamp accuracy, validated for over 10 days of continuous operation at low power.
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An Adaptive Spatiotemporal Clustering Framework for 3D Ocean Subsurface Temperature Reconstruction
An adaptive spatiotemporal clustering framework boosts deep learning reconstruction of global ocean subsurface temperature fields from surface data, delivering 12.4% to 27.2% RMSE improvements when paired with models such as DP-CNN, Attention U-Net, and ViT.