{"title":"Generative Model Study for 1+1d-Complex Scalar Field Theory","authors":"K. Zhou, G. Endrődi, L. Pang, H. Stoecker","doi":"10.22323/1.372.0007","DOIUrl":"https://doi.org/10.22323/1.372.0007","url":null,"abstract":"We reported a recent work that applies modern Deep Learning (convolutional neural network) techniques in the context of two dimensional lattice complex scalar field theory, which has a non-trivial phase diagram at nonzero temperature and chemical potential. Especially we introuced the field configuration production with generative adversarial network (GAN), where the GAN is showed to be able to automatically capture the implicit local constraint for the physical configurations and also the underlying physical distribution. We further explored generalize the configuration production at different parameter space using conditional GAN.","PeriodicalId":437580,"journal":{"name":"Proceedings of Artificial Intelligence for Science, Industry and Society — PoS(AISIS2019)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128453564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Studying the parton content of the proton with deep learning models","authors":"J. Cruz-Martinez, S. Carrazza, Roy Stegeman","doi":"10.22323/1.372.0008","DOIUrl":"https://doi.org/10.22323/1.372.0008","url":null,"abstract":"Parton Distribution Functions (PDFs) model the parton content of the proton. Among the many collaborations which focus on PDF determination, NNPDF pioneered the use of Neural Networks to model the probability of finding partons (quarks and gluons) inside the proton with a given energy and momentum. In this proceedings we make use of state of the art techniques to modernize the NNPDF methodology and study different models and optimizers in order to improve the quality of the PDF: improving both the quality and efficiency of the fits. We also present the evolutionary_keras library, a Keras implementation of the Evolutionary Algorithms used by NNPDF.","PeriodicalId":437580,"journal":{"name":"Proceedings of Artificial Intelligence for Science, Industry and Society — PoS(AISIS2019)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115345163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}