{"@context":{"@language":"en","@vocab":"https://schema.org/","citeAs":"cr:citeAs","column":"cr:column","conformsTo":"dct:conformsTo","cr":"http://mlcommons.org/croissant/","rai":"http://mlcommons.org/croissant/RAI/","data":{"@id":"cr:data","@type":"@json"},"dataType":{"@id":"cr:dataType","@type":"@vocab"},"dct":"http://purl.org/dc/terms/","examples":{"@id":"cr:examples","@type":"@json"},"extract":"cr:extract","field":"cr:field","fileProperty":"cr:fileProperty","fileObject":"cr:fileObject","fileSet":"cr:fileSet","format":"cr:format","includes":"cr:includes","isLiveDataset":"cr:isLiveDataset","jsonPath":"cr:jsonPath","key":"cr:key","md5":"cr:md5","parentField":"cr:parentField","path":"cr:path","recordSet":"cr:recordSet","references":"cr:references","regex":"cr:regex","repeated":"cr:repeated","replace":"cr:replace","sc":"https://schema.org/","separator":"cr:separator","source":"cr:source","subField":"cr:subField","transform":"cr:transform","wd":"https://www.wikidata.org/wiki/"},"@type":"sc:Dataset","conformsTo":"http://mlcommons.org/croissant/1.0","name":"Uncovering turbulent plasma dynamics via deep learning from partial observations","url":"https://doi.org/10.7910/DVN/JNMLN2","creator":[{"@type":"Person","givenName":"Jerry David Ben Barrett Rogers","name":"Abhilash Mathews, Manaure Francisquez, Jerry Hughes, David Hatch, Ben Zhu, Barrett Rogers"}],"description":"One of the most intensely studied aspects of magnetic confinement fusion is edge plasma turbulence which is critical to reactor performance and operation. Drift-reduced Braginskii two-fluid theory has for decades been widely applied to model boundary plasmas with varying success. Towards better understanding edge turbulence in both theory and experiment, we demonstrate that a novel multi-network physics-informed deep learning framework constrained by partial differential equations can accurately learn turbulent fields consistent with the two-fluid theory from partial observations of electron pressure which is not otherwise possible using conventional equilibrium models. This technique presents a novel paradigm for the advanced design of plasma diagnostics and validation of magnetized plasma turbulence theories in challenging thermonuclear environments.","keywords":["Physics","edge transport","machine learning","magnetized plasmas","scrape-off layer plasmas","two fluid theory"],"license":"https://dataverse.harvard.edu/api/datasets/:persistentId/versions/2.0/customlicense?persistentId=doi:10.7910/DVN/JNMLN2","datePublished":"2021-05-24","dateModified":"2022-11-11","includedInDataCatalog":{"@type":"DataCatalog","name":"Harvard Dataverse","url":"https://dataverse.harvard.edu"},"publisher":{"@type":"Organization","name":"Harvard Dataverse"},"version":"2.0","citeAs":"@data{DVN/JNMLN2_2021,author = {Abhilash Mathews, Manaure Francisquez, Jerry Hughes, David Hatch, Ben Zhu, Barrett Rogers},publisher = {Harvard Dataverse},title = {Uncovering turbulent plasma dynamics via deep learning from partial observations},year = {2021},url = {https://doi.org/10.7910/DVN/JNMLN2}}","distribution":[{"@type":"cr:FileObject","@id":"21ja011_archival_manuscript.pdf","name":"21ja011_archival_manuscript.pdf","encodingFormat":"application/pdf","md5":"f0168481307b38939f4d7ef99714312b","contentSize":"6600983","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/6706044"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig1_data.h5","name":"21ja011_fig1_data.h5","encodingFormat":"application/x-h5","md5":"bf22c7926d4154e2fee4094d649f05ed","contentSize":"368640","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730546"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig1_image.png","name":"21ja011_fig1_image.png","encodingFormat":"image/png","md5":"77ed442c1e3e0da88086ad2620a40cbb","contentSize":"142365","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730540"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig2_data.h5","name":"21ja011_fig2_data.h5","encodingFormat":"application/x-h5","md5":"8d376e26a725471cd2fc02d1c5824535","contentSize":"8432","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730543"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig2_image.png","name":"21ja011_fig2_image.png","encodingFormat":"image/png","md5":"a6e48379ec133a1ea1fd936afc318a44","contentSize":"44890","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730536"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig3_data.png","name":"21ja011_fig3_data.png","encodingFormat":"image/png","md5":"5aa79e38b4455e50c5344e7d8c8b741f","contentSize":"172029","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730538"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig3_image.png","name":"21ja011_fig3_image.png","encodingFormat":"image/png","md5":"5aa79e38b4455e50c5344e7d8c8b741f","contentSize":"172029","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730532"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig4_data.h5","name":"21ja011_fig4_data.h5","encodingFormat":"application/x-h5","md5":"9f5665920331e381da9e655e5d65a9f3","contentSize":"318088","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730534"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig4_image.png","name":"21ja011_fig4_image.png","encodingFormat":"image/png","md5":"15197a41116694b9ed0140e668058800","contentSize":"127779","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730539"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig5_data.h5","name":"21ja011_fig5_data.h5","encodingFormat":"application/x-h5","md5":"c77d929dde8999ee46a255aa7c0ec1ea","contentSize":"4568","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730535"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig5_image.png","name":"21ja011_fig5_image.png","encodingFormat":"image/png","md5":"1e90b225260f6d6b491d5331d1c91733","contentSize":"60041","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730547"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig6_data.h5","name":"21ja011_fig6_data.h5","encodingFormat":"application/x-h5","md5":"db460f2c9582fc27b342b3c1e7c5176a","contentSize":"318088","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730545"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig6_image.png","name":"21ja011_fig6_image.png","encodingFormat":"image/png","md5":"48ee8263571fc64f33fd7bed9c351bd3","contentSize":"150176","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730544"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig7_data.h5","name":"21ja011_fig7_data.h5","encodingFormat":"application/x-h5","md5":"492027ace8e6c232d0de095ec96c7036","contentSize":"124216","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730533"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig7_image.png","name":"21ja011_fig7_image.png","encodingFormat":"image/png","md5":"d9a7259ed96dd542c75be45689ed9c23","contentSize":"251672","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730542"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig8_data.h5","name":"21ja011_fig8_data.h5","encodingFormat":"application/x-h5","md5":"a8f1fcc596c8ab4c43285ef37f07c8d0","contentSize":"362944","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730541"},{"@type":"cr:FileObject","@id":"var/www/html/reports/21JA/21JA11/21ja011_fig8_image.png","name":"21ja011_fig8_image.png","encodingFormat":"image/png","md5":"60c145dffb17e171d2e379d411caf5f1","contentSize":"170695","description":"","contentUrl":"https://dataverse.harvard.edu/api/access/datafile/4730537"}]}