# Effective Benchmarks for Optical Turbulence Modeling

arXiv: https://arxiv.org/abs/2401.03573 · HTML: https://mlsanity.com/paper/2401.03573/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 7 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [WeatherBench: A benchmark dataset for data-driven weather forecasting](https://mlsanity.com/paper/2002.00469/) (2020)
- [Hybrid Optical Turbulence Models Using Machine Learning and Local Measurements](https://mlsanity.com/paper/2310.17829/) (2023)

## Abstract (arXiv)

Optical turbulence presents a significant challenge for communication, directed energy, and imaging systems, especially in the atmospheric boundary layer. Effective modeling of optical turbulence strength is critical for the development and deployment of these systems. The lack of standard evaluation tools, especially long-term data sets, modeling tasks, metrics, and baseline models, prevent effective comparisons between approaches and models. This reduces the ease of reproducing results and contributes to over-fitting on local micro-climates. Performance characterized using evaluation metrics provides some insight into the applicability of a model for predicting the strength of optical turbulence. However, these metrics are not sufficient for understanding the relative quality of a model. We introduce the textttotbench package, a Python package for rigorous development and evaluation of optical turbulence strength prediction models. The package provides a consistent interface for evaluating optical turbulence models on a variety of benchmark tasks and data sets. The textttotbench package includes a range of baseline models, including statistical, data-driven, and deep learning models, to provide a sense of relative model quality. textttotbench also provides support for adding new data sets, tasks, and evaluation metrics. The package is available at urlhttps://github.com/cdjellen/otbench.