Generative Adversarial Networks (GAN) for compact beam source modelling in Monte Carlo simulations

@article{Sarrut2019GenerativeAN,
  title={Generative Adversarial Networks (GAN) for compact beam source modelling in Monte Carlo simulations},
  author={David Sarrut and Nils Krah and Jean-Michel L{\'e}tang},
  journal={Physics in medicine and biology},
  year={2019}
}
  • David Sarrut, Nils Krah, Jean-Michel Létang
  • Published in
    Physics in medicine and…
    2019
  • Physics, Computer Science, Medicine
  • A method is proposed and evaluated to model large and inconvenient phase space files used in Monte Carlo simulations by a compact Generative Adversarial Network (GAN). The GAN is trained based on a phase space dataset to create a neural network, called Generator (G), allowing G to mimic the multidimensional data distribution of the phase space. At the end of the training process, G is stored with about 0.5 million weights, around 10\,MB, instead of few GB of the initial file. Particles are then… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 25 REFERENCES

    Wasserstein GAN

    VIEW 13 EXCERPTS
    HIGHLY INFLUENTIAL

    Generative Adversarial Nets

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    The GAN Zoo

    • A. Hindupur
    • Technical report, https://github.com/hindupuravinash/ the-gan-zoo. (consulted June
    • 2019
    VIEW 1 EXCERPT

    Generative Adversarial Networks: An Overview

    VIEW 1 EXCERPT

    Automatic differentiation in PyTorch

    VIEW 1 EXCERPT

    The physics of small megavoltage photon beam dosimetry.

    • Pedro Andreo
    • Medicine, Physics
    • Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
    • 2017
    VIEW 1 EXCERPT