From 780a39bff714c856db2484685369c5154f5b08b9 Mon Sep 17 00:00:00 2001 From: "netlify[bot]" Date: Fri, 27 Mar 2026 05:06:56 +0000 Subject: [PATCH] Page not found error during deployment (69c60f574cff941f366ea50a) --- netlify.toml | 2 + public/index.html | 248 ++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 250 insertions(+) create mode 100644 netlify.toml create mode 100644 public/index.html diff --git a/netlify.toml b/netlify.toml new file mode 100644 index 0000000..c7bd82b --- /dev/null +++ b/netlify.toml @@ -0,0 +1,2 @@ +[build] + publish = "public" diff --git a/public/index.html b/public/index.html new file mode 100644 index 0000000..f90c59c --- /dev/null +++ b/public/index.html @@ -0,0 +1,248 @@ + + + + + + Beam Weaver — Physics-informed RL for Photon Transport + + + +
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Beam Weaver

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Physics-informed n-step reinforcement learning for stochastic photon transport in water

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+ DOI + JOSS status +
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+ Status: research prototype + License: Apache-2.0 + Domain: radiation transport & AI +
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+ Beam Weaver is a research software prototype for learning event-by-event photon transport in a homogeneous water phantom. It combines a custom Monte Carlo reference simulator, a physics-informed n-step Soft Actor-Critic reinforcement learning environment, and evaluation tools that compare learned particle histories against conventional transport outputs. +
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What Beam Weaver Does

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Beam Weaver explores whether a Soft Actor-Critic (SAC) agent can learn the stochastic logic of photon transport in water at the event level, rather than only predicting final observables.

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For each photon history, the code predicts:

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  • The next interaction type
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  • The distance to the next interaction
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  • Scattering geometry
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  • The outgoing photon state
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  • The number, energy, and direction of secondary particles
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This makes Beam Weaver a learned transport-engine prototype rather than a generic machine-learning regressor.

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Photon Interactions Modeled

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Rayleigh Scattering

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Coherent scattering with energy-dependent form factors for water.

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Compton Scattering

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Incoherent scattering with recoil electron kinematics.

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Photoelectric Absorption

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Shell-specific absorption with electron emission.

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Pair Production

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Electron-positron pair creation with energy sharing.

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Training Architecture

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Beam Weaver is trained in two stages:

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  • Stage I — Pretraining: Supervised learning of a multi-head physics branch on Monte Carlo-generated labels (interaction type, free path, scattering angles, secondary particles).
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  • Stage II — Curriculum RL: A five-phase curriculum that progressively transitions from teacher-forced Monte Carlo supervision to autonomous policy learning using a hybrid n-step SAC framework.
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The RL objective is physics-regularized, combining the standard SAC loss with auxiliary physics losses for energy, angle, and interaction-type consistency.

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Quick Start

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# Create environment
+conda env create -f environment.yml
+conda activate beam-weaver
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+# Or with pip
+python -m venv .venv
+source .venv/bin/activate
+pip install -r requirements.txt
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+# Run Beam Weaver
+python Beam_weaver_0.1.0.py
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The script is interactive and supports: generating Monte Carlo data, pretraining the physics head, training a SAC model, evaluating trained models, and comparing MC vs. agent outputs.

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Authors

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Pedro Teles
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Assistant Professor, Department of Physics and Astronomy, Faculty of Sciences, University of Porto
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João Melo
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MSc Student in Medical Physics, Department of Physics and Astronomy, Faculty of Sciences, University of Porto
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References

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  • F. Salvat. PENELOPE-2018: A Code System for Monte Carlo Simulation of Electron and Photon Transport. OECD/NEA, 2019.
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  • T. Haarnoja et al. Soft Actor-Critic: Off-Policy Maximum Entropy Deep RL with a Stochastic Actor. ICML, 2018.
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  • A. Raffin et al. Stable-Baselines3: Reliable RL Implementations. JMLR, 22:1–8, 2021.
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  • J. H. Hubbell et al. Atomic Form Factors, Incoherent Scattering Functions, and Photon Scattering Cross Sections. J. Phys. Chem. Ref. Data, 4(3), 1975.
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