Distilling deep optical flow stereo methods to retrieve dense three-dimensional wind fields

arXiv:2609.03100 · cs.LG, physics.ao-ph · Submitted 2026-09-02 · Read on arXiv

cs.LG, physics.ao-ph

Submitted: 2026-09-02

Updated: 2026-09-02

Code: https://github.com/tjvandal/windflow

Terminology

• Optical Flow — 49× in this paper · explained in 2 episodes
• Distillation — 18× in this paper · explained in 2 episodes
• Fine-Tuning — 18× in this paper · explained in 5 episodes
• Consistency — 7× in this paper · explained in 3 episodes
• Knowledge Distillation — 7× in this paper · explained in 11 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Data Assimilation — 3× in this paper · explained in 2 episodes
• Integrated Global Radiosonde Archive — 3× in this paper
• Regularization — 3× in this paper · explained in 3 episodes
• Calibration — 2× in this paper · explained in 7 episodes
• Cloud Profiling Radar — 2× in this paper
• Core — 2× in this paper · explained in 2 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Numerical Weather Prediction — 2× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Derived Motion Wind Vectors — 1× in this paper
• Fidelity — 1× in this paper · explained in 2 episodes
• National Aeronautics and Space Administration — 1× in this paper
• Quantifying Uncertainty — 1× in this paper · explained in 5 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• West Advanced Baseline Imager — 1× in this paper

Sources

• Distilling the Knowledge in a Neural Network
• Adam: A Method for Stochastic Optimization

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