# Copula-based synthetic data augmentation for machine-learning emulators

arXiv: https://arxiv.org/abs/2012.09037 · HTML: https://mlsanity.com/paper/2012.09037/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Marginal Tail-Adaptive Normalizing Flows](https://mlsanity.com/paper/2206.10311/) (2022)
- [Machine Learning Emulation of 3D Cloud Radiative Effects](https://mlsanity.com/paper/2103.11919/) (2021)
- [CopulaSMOTE: A Copula-Based Oversampling Approach for Imbalanced Classification in Diabetes Prediction](https://mlsanity.com/paper/2506.17326/) (2025)
- [Machine Learning Emulation of Urban Land Surface Processes](https://mlsanity.com/paper/2112.11429/) (2021)
- [Binary Gaussian Copula Synthesis: an LLM-powered data augmentation framework for early dialysis prediction in chronic kidney disease](https://mlsanity.com/paper/2403.00965/) (2024)
- [TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates](https://mlsanity.com/paper/2605.17642/) (2026)

## Abstract (arXiv)

Can we improve machine-learning (ML) emulators with synthetic data? If data are scarce or expensive to source and a physical model is available, statistically generated data may be useful for augmenting training sets cheaply. Here we explore the use of copula-based models for generating synthetically augmented datasets in weather and climate by testing the method on a toy physical model of downwelling longwave radiation and corresponding neural network emulator. Results show that for copula-augmented datasets, predictions are improved by up to 62 % for the mean absolute error (from 1.17 to 0.44 W m-2).