Cargando…
Model-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production
<!--HTML-->We propose and demonstrate the use of a Model-Assisted Generative Adversarial Network to produce simulated images that accurately match true images through the variation of underlying model parameters that describe the image generation process. The generator learns the parameter val...
Autor principal: | |
---|---|
Lenguaje: | eng |
Publicado: |
2019
|
Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2672623 |
_version_ | 1780962468442406912 |
---|---|
author | Alonso Monsalve, Saul |
author_facet | Alonso Monsalve, Saul |
author_sort | Alonso Monsalve, Saul |
collection | CERN |
description | <!--HTML-->We propose and demonstrate the use of a Model-Assisted Generative Adversarial Network to produce simulated images that accurately match true images through the variation of underlying model parameters that describe the image generation process. The generator learns the parameter values that give images that best match the true images. The best match parameter values that produce the most accurate simulated images can be extracted and used to re-tune the default simulation to minimise any bias when applying image recognition techniques to simulated and true images. In the case of a real-world experiment, the true data is replaced by experimental data with unknown true parameter values. The Model-Assisted Generative Adversarial Network uses a convolutional neural network to emulate the simulation for all parameter values that, when trained, can be used as a conditional generator for fast image production. |
id | cern-2672623 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2019 |
record_format | invenio |
spelling | cern-26726232022-11-02T22:33:36Zhttp://cds.cern.ch/record/2672623engAlonso Monsalve, SaulModel-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production3rd IML Machine Learning WorkshopLPCC Workshops<!--HTML-->We propose and demonstrate the use of a Model-Assisted Generative Adversarial Network to produce simulated images that accurately match true images through the variation of underlying model parameters that describe the image generation process. The generator learns the parameter values that give images that best match the true images. The best match parameter values that produce the most accurate simulated images can be extracted and used to re-tune the default simulation to minimise any bias when applying image recognition techniques to simulated and true images. In the case of a real-world experiment, the true data is replaced by experimental data with unknown true parameter values. The Model-Assisted Generative Adversarial Network uses a convolutional neural network to emulate the simulation for all parameter values that, when trained, can be used as a conditional generator for fast image production.oai:cds.cern.ch:26726232019 |
spellingShingle | LPCC Workshops Alonso Monsalve, Saul Model-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production |
title | Model-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production |
title_full | Model-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production |
title_fullStr | Model-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production |
title_full_unstemmed | Model-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production |
title_short | Model-Assisted GANs for the optimisation of simulation parameters and as an algorithm for fast Monte Carlo production |
title_sort | model-assisted gans for the optimisation of simulation parameters and as an algorithm for fast monte carlo production |
topic | LPCC Workshops |
url | http://cds.cern.ch/record/2672623 |
work_keys_str_mv | AT alonsomonsalvesaul modelassistedgansfortheoptimisationofsimulationparametersandasanalgorithmforfastmontecarloproduction AT alonsomonsalvesaul 3rdimlmachinelearningworkshop |