# Time to Market Reduction for Hydrogen Fuel Cell Stacks using Generative Adversarial Networks

arXiv: https://arxiv.org/abs/2212.11733 · HTML: https://mlsanity.com/paper/2212.11733/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 22 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [How good is my GAN?](https://mlsanity.com/paper/1807.09499/) (2018)
- [A deep generative model for probabilistic energy forecasting in power systems: normalizing flows](https://mlsanity.com/paper/2106.09370/) (2021)
- [Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation](https://mlsanity.com/paper/1811.04172/) (2018)
- [A Novel Semi-Supervised Data-Driven Method for Chiller Fault Diagnosis with Unlabeled Data](https://mlsanity.com/paper/2011.00187/) (2020)
- [Impedance-based Capacity Estimation for Lithium-Ion Batteries Using Generative Adversarial Network](https://mlsanity.com/paper/2107.06177/) (2021)

## Abstract (arXiv)

To face the dependency on fossil fuels and limit carbon emissions, fuel cells are a very promising technology and appear to be a key candidate to tackle the increase of the energy demand and promote the energy transition. To meet future needs for both transport and stationary applications, the time to market of fuel cell stacks must be drastically reduced. Here, a new concept to shorten their development time by introducing a disruptive and highefficiency data augmentation approach based on artificial intelligence is presented. Our results allow reducing the testing time before introducing a product on the market from a thousand to a few hours. The innovative concept proposed here can support engineering and research tasks during the fuel cell development process to achieve decreased development costs alongside a reduced time to market.