# Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers

arXiv: https://arxiv.org/abs/1711.02702 · HTML: https://mlsanity.com/paper/1711.02702/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms](https://mlsanity.com/paper/2203.12114/) (2022)
- [Generative Model Predictive Control in Manufacturing Processes: A Review](https://mlsanity.com/paper/2511.17865/) (2025)
- [Learning Nonlinear Waves in Plasmon-induced Transparency](https://mlsanity.com/paper/2108.01508/) (2021)

## Abstract (arXiv)

Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require em intelligent algorithms capable of inferring approximate models of the underlying physics and discovering appropriate control laws in order to maintain robust performance for a given objective. In this work, we demonstrate the first integration of a em deep learning (DL) architecture with em model predictive control (MPC) in order to self-tune a mode-locked fiber laser. Not only can our DL-MPC algorithmic architecture approximate the unknown fiber birefringence, it also builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence. We demonstrate the effectiveness of this method on a fiber laser which is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers.