# Deep Learning Based Reconstruction of Total Solar Irradiance

arXiv: https://arxiv.org/abs/2107.11042 · HTML: https://mlsanity.com/paper/2107.11042/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 23 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification](https://mlsanity.com/paper/1711.05859/) (2017)
- [Learning Scalable Deep Kernels with Recurrent Structure](https://mlsanity.com/paper/1610.08936/) (2016)
- [Identifying and Tracking Solar Magnetic Flux Elements with Deep Learning](https://mlsanity.com/paper/2008.12080/) (2020)
- [Differential Privacy for Stackelberg Games](https://mlsanity.com/paper/2002.00944/) (2020)
- [Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift](https://mlsanity.com/paper/2006.10990/) (2020)

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

- [A Deep Learning Approach to Dst Index Prediction](https://mlsanity.com/paper/2205.02447/) (2022)

## Abstract (arXiv)

The Earth's primary source of energy is the radiant energy generated by the Sun, which is referred to as solar irradiance, or total solar irradiance (TSI) when all of the radiation is measured. A minor change in the solar irradiance can have a significant impact on the Earth's climate and atmosphere. As a result, studying and measuring solar irradiance is crucial in understanding climate changes and solar variability. Several methods have been developed to reconstruct total solar irradiance for long and short periods of time; however, they are physics-based and rely on the availability of data, which does not go beyond 9,000 years. In this paper we propose a new method, called TSInet, to reconstruct total solar irradiance by deep learning for short and long periods of time that span beyond the physical models' data availability. On the data that are available, our method agrees well with the state-of-the-art physics-based reconstruction models. To our knowledge, this is the first time that deep learning has been used to reconstruct total solar irradiance for more than 9,000 years.