# Structured Deep Kernel Networks for Data-Driven Closure Terms of Turbulent Flows

arXiv: https://arxiv.org/abs/2103.13655 · HTML: https://mlsanity.com/paper/2103.13655/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 25 Mar 2021.

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

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [A representer theorem for deep kernel learning](https://mlsanity.com/paper/1709.10441/) (2017)
- [A Perspective on Machine Learning Methods in Turbulence Modelling](https://mlsanity.com/paper/2010.12226/) (2020)
- [A machine learning framework for LES closure terms](https://mlsanity.com/paper/2010.03030/) (2020)

## Built on by (most cited first, 1 of 1)

- [Analysis of Structured Deep Kernel Networks](https://mlsanity.com/paper/2105.07228/) (2021)

## Abstract (arXiv)

Standard kernel methods for machine learning usually struggle when dealing with large datasets. We review a recently introduced Structured Deep Kernel Network (SDKN) approach that is capable of dealing with high-dimensional and huge datasets - and enjoys typical standard machine learning approximation properties. We extend the SDKN to combine it with standard machine learning modules and compare it with Neural Networks on the scientific challenge of data-driven prediction of closure terms of turbulent flows. We show experimentally that the SDKNs are capable of dealing with large datasets and achieve near-perfect accuracy on the given application.