Add shaping laser pulses blogpost

_blog.yml

@@ -6361,3 +6361,15 @@
     - community
     - research
     - open-source-collab 
+
+- local: shaping-laser-pulses
+  title: "Shaping Laser Pulses with Reinforcement Learning"
+  author: fracapuano
+  thumbnail: https://huggingface.co/datasets/fracapuano/rlaser-assets/resolve/main/assets/Figure1.png
+  date: July 16, 2025
+  tags:
+    - reinforcement-learning
+    - physics
+    - ml-for-science
+    - research
+    - open-source

shaping-laser-pulses.md

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+---
+title: Shaping Laser Pulses with Reinforcement Learning
+thumbnail: https://huggingface.co/datasets/fracapuano/rlaser-assets/resolve/main/assets/Figure1.png
+authors:
+- user: fracapuano
+---
+
+# Table of Contents
+- [TL;DR](#tl-dr)
+- [Shaping Laser Pulses](#shaping-laser-pulses)
+- [Automated approaches](#automated-approaches)
+- [BO's limitations](#bos-limitations)
+- [RL to the rescue](#rl-to-the-rescue)
+
+
+## TL; DR: 
+We train a Reinforcement Learning agent to **optimally shape laser pulses** from readily-available diagnostics images, across a range of dynamics parameters for intensity maximization.
+Our method **(1) completely bypasses imprecise reconstructions** of ultra-fast laser pulses, **(2) can learn to be robust to varying dynamics** and **(3) prevents erratic behavior** at test-time by training in coarse simulation only.
+
+<div align="center">
+    <img src="https://huggingface.co/datasets/fracapuano/rlaser-assets/resolve/main/assets/Figure1_and_CPA.png" alt="Phase changes animation">
+    <p> (A) Schematic representation of the RL pipeline for pulse shaping in HPL systems. (B) Illustration of the process of linear and non-linear phase accumulation taking place along the pump-chain of laser systems.</p>
+</div>
+
+By opportunely controlling the phase imposed at the stretcher, one can benefit from both energy and duration gains, for maximal peak intensity.
+
+---
+
+## Shaping Laser Pulses
+
+Ultra-fast light-matter interactions, such as laser-plasma physics and nonlinear optics, require precise shaping of the temporal pulse profile.
+Optimizing such profiles is one of the most critical tasks to establish control over these interactions. 
+Typically, the highest intensities conveyed by laser pulses can usually be achieved by compressing a pulse to its transform-limited (TL) pulse shape, while some interactions may require arbitrary temporal shapes different from the TL profile (mainly to protect the system from potential damage).
+
+
+<div align="center">
+    <img src="https://huggingface.co/datasets/fracapuano/rlaser-assets/resolve/main/assets/phase.gif" alt="Phase changes animation">
+    <p>Changes in the spectral phase applied on the input spectrum (left) have a direct impact on the temporal profile (right).</p>
+</div>
+
+In this work, we shape laser pulses by varying the GDD, TOD and FOD coefficients, effectively tuning the spectral phase applied to minimize temporal pulse duration.
+
+<!-- add link to space demo -->
+
+## Automated approaches
+
+The most common automated laser pulse shape optimization approaches mainly employ black-box algorithms, such as Bayesian Optimization (BO) and Evolutionary Strategies (ES). These algorithms are typically used in a closed feedback loop between the pulse shaper and various measurement devices.
+
+For pulse duration minimization, numerical methods including BO and ES require precise temporal shape reconstruction, to measure the loss against a target temporal profile, or obtain derived metrics such as duration at full-width half-max, or peak intensity value.
+
+Recently, approaches based on BO have gained popularity because of their broad applicability and sample efficiency over ES, often requiring a fraction of the function evaluations to obtain comparable performance.
+Indeed, in automated pulse shaping, each function evaluation requires one (or more) real-world laser bursts. Therefore, methods that directly optimize real-world operational hardware are evaluated based on their efficiency in terms of number of the required interactions.
+
+### BO's limitations 
+
+While effective, BO suffers from limitations related to (1) the need to perform precise pulse reconstruction (2) machine-safety and (3) transferability. To a large extent, these limitations are only more significant for other methods such as ES.
+
+#### 1. Imprecise pulse reconstruction
+BO requires accurate measurements of the current pulse shape to guide optimization. However, real-world pulse reconstruction techniques can be **noisy or imprecise**, leading to poor state estimation, and increasingly high risk of applying suboptimal controls.
+
+<div align="center">
+    <img src="https://huggingface.co/datasets/fracapuano/rlaser-assets/resolve/main/assets/reconstructing_frog.png" alt="Phase changes animation" width="70%">
+    <p>Temporal profiles with temporal-domain reconstructed phase (top) versus diagnostic measures of the burst status (bottom), in the form of FROG traces. Image source: Zahavy et al., 2018.</p>
+</div>
+
+#### 2. Dependancy on the dynamics
+BO typically optimizes for specific system parameters and **doesn't generalize well when laser dynamics change**. Each new experimental setup or parameter regime may require re-optimizing the process from scratch!
+
+This follows from standard BO optimizing a typically-scalar loss function under stationarity assumptions, which can prove rather problematic in the context of pulse-shaping. This follows from the fact day-to-day changes in the experimental setup can quite reasonably result in non-stationarity: **the same control, when applied in different experimental conditions, can yield significantly different results**.
+
+<div align="center">
+    <img src="https://huggingface.co/datasets/fracapuano/rlaser-assets/resolve/main/assets/B_integral.png" alt="Phase changes animation" width="70%">
+    <p>Impact of experimental conditions only, in this case a non-linearity parameter known as "B-integral", on the end-result of applying the same control.</p>
+</div>
+
+#### 3. Erratic exploration
+
+BO can endanger the system by applying **abrupt controls at initialization**. Controls are applied as temperature gradients applied on a gated-optical fiber, and as such successive controls cannot typically vary significantly because the one-step difference in temperature difference cannot vary arbitrarily.
+
+<div align="center" style="display: flex; justify-content: center; gap: 20px;">
+    <div>
+        <img src="https://huggingface.co/datasets/fracapuano/rlaser-assets/resolve/main/assets/pulses_anim.gif" alt="BO temporal profile">
+    </div>
+    <div>
+        <img src="https://huggingface.co/datasets/fracapuano/rlaser-assets/resolve/main/assets/control_anim.gif" alt="BO exploration">
+    </div>
+</div>
+<p>BO, (left) temporal profile obtained probing points from the parameters space and (right) BO, evolution of the probed points as the parameters space is explored.</p>
+
+## RL to the rescue
+
+In this work, we address all these limitations by **(1) learning policies directly from readily-available images**, capable of **(2) working across varying dynamics**, and **(3) trained in coarse simulation to prevent erratic-behavior** at test time.
+
+First, (1) we train our RL agent directly from readily available diagnostic measurements in the form of 64x64 images. This means we can **entirely bypass the reconstruction noise** arising from numerical methods for temporal pulse-shape reconstruction, learning straight from single-channel images.
+
+<div align="center">
+    <img src="https://huggingface.co/datasets/fracapuano/rlaser-assets/resolve/main/assets/Figure1.png" width="50%">
+    <p>Control is applied directly from images, thus learning to adjust to unmodeled changes in the environment. </p>
+</div>
+
+Further, (2) by training on diverse scenarios, RL can develop both **safe and general control strategies** adaptive to a range of different dynamics. In turn, this allows to run and lively update control policies across experimental conditions.
+<div align="center">
+    <img src="https://huggingface.co/datasets/fracapuano/rlaser-assets/resolve/main/assets/udr_vs_doraemon_average.png" width="50%">
+    <p>We can retain high level of performance (>70%) even for larger---above 5, fictional---levels of non-linearity in the systems. This shows we can retain performance by applying a proper randomization technique.</p>
+</div>
+
+Lastly, (3) by learning in a corse simulation, we can **drastically limit the number of interactions at test time**, preventing erratic behavior which would endanger system's safety.
+
+<div align="center">
+    <img src="https://huggingface.co/datasets/fracapuano/rlaser-assets/resolve/main/assets/machinesafety.png" width="50%">
+    <p> Controls applied (BO vs RL). As it samples from an iteratively-refined surrogate model of the objective function, BO explores much more erratically than RL.</p>
+</div>
+
+In conclusion, we demonstrate that deep reinforcement learning can master laser pulse shaping by learning **robust policies from raw diagnostics**, paving the way towards **autonomous control of complex physical systems**.
+
+If you're interested in learning more, check out [our latest paper](https://huggingface.co/papers/2503.00499), our [simulator's code](https://github.com/fracapuano/gym-laser), and try out the [live demo](https://huggingface.co/spaces/fracapuano/RLaser).