The hybrid model couples existing macro-meteorological models developed for similar microclimates along with some minimal amount of locally-acquired meteorological and $C_n^2$ data. The hybrid model framework consists of two components, a baseline macro-meteorological model and a machine learning model trained on that baseline macro-meteorological model’s residual error over the locally-acquired training measurements.

Vision-aided GAN training involves using pretrained computer vision models in an ensemble of discriminators to improve GAN performance. Linear separability between real and fake samples in pretrained model embeddings is used as a measure to choose the most accurate pretrained models for a dataset.

Vision-aided GAN

Ensembling Off-the-shelf Models for GAN Training

Hybrid AWT

Hybrid Optical Turbulence Models Using Machine Learning and Local Measurements

An **Octave Convolution (OctConv)** stores and process feature maps that vary spatially “slower” at a lower spatial resolution reducing both memory and computation cost. It takes in feature maps containing tensors of two frequencies one octave apart, and extracts information directly from the
low-frequency maps without the need of decoding it back to the high-frequency. The motivation is that in natural images, information is conveyed at different frequencies where higher frequencies are usually encoded with fine details and lower frequencies are usually encoded with global structures.

Source	Hybrid Optical Turbulence Models Using Machine Learning and Local Measurements
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com