Deep Learning Reconstruction of Ultra-Short Pulses
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Mar 2018.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
5 papers.
- 2025Physics-informed time series analysis with Kolmogorov-Arnold Networks under Ehrenfest constraintscited by 5
- 2022Data-driven Science and Machine Learning Methods in Laser-Plasma Physicscited by 4
- 2021Acoustic Structure Inverse Design and Optimization Using Deep Learningcited by 1
- 2025Shaping Laser Pulses with Reinforcement Learningcited by 1
- 2026Digital Twin Modeling of Quantum Dynamical Systems: Dissipative Quantum Reservoir Computingcited by 0
- 2026Digital Twin Modeling of Quantum Dynamical Systems: Dissipative Quantum Reservoir Computing29 Sep 2026
- 2025Physics-informed time series analysis with Kolmogorov-Arnold Networks under Ehrenfest constraints23 Sep 2025
- 2025Shaping Laser Pulses with Reinforcement Learning1 Mar 2025
- 2022Data-driven Science and Machine Learning Methods in Laser-Plasma Physics30 Nov 2022
- 2021Acoustic Structure Inverse Design and Optimization Using Deep Learning29 Jan 2021