# Deep Learning Reconstruction of Ultra-Short Pulses

arXiv: https://arxiv.org/abs/1803.06024 · HTML: https://mlsanity.com/paper/1803.06024/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2018.

## Built on by (most cited first, 5 of 5)

- [Physics-informed time series analysis with Kolmogorov-Arnold Networks under Ehrenfest constraints](https://mlsanity.com/paper/2509.18483/) (2025)
- [Data-driven Science and Machine Learning Methods in Laser-Plasma Physics](https://mlsanity.com/paper/2212.00026/) (2022)
- [Acoustic Structure Inverse Design and Optimization Using Deep Learning](https://mlsanity.com/paper/2102.02063/) (2021)
- [Shaping Laser Pulses with Reinforcement Learning](https://mlsanity.com/paper/2503.00499/) (2025)
- [Digital Twin Modeling of Quantum Dynamical Systems: Dissipative Quantum Reservoir Computing](https://mlsanity.com/paper/2609.36901/) (2026)

## Abstract (arXiv)

Ultra-short laser pulses with femtosecond to attosecond pulse duration are the shortest systematic events humans can create. Characterization (amplitude and phase) of these pulses is a key ingredient in ultrafast science, e.g., exploring chemical reactions and electronic phase transitions. Here, we propose and demonstrate, numerically and experimentally, the first deep neural network technique to reconstruct ultra-short optical pulses. We anticipate that this approach will extend the range of ultrashort laser pulses that can be characterized, e.g., enabling to diagnose very weak attosecond pulses.