{"id":2,"date":"2021-11-19T11:03:01","date_gmt":"2021-11-19T11:03:01","guid":{"rendered":"https:\/\/presage.lis-lab.fr\/?page_id=2"},"modified":"2022-12-28T17:28:24","modified_gmt":"2022-12-28T17:28:24","slug":"sample-page","status":"publish","type":"page","link":"https:\/\/presage.lis-lab.fr\/","title":{"rendered":"PRESAGE"},"content":{"rendered":"\n<p>Welcome to the PRESAGE project!<br>PRESAGE stands for PREdicting Solar Activity using machine learning on heteroGEneous data.<\/p>\n\n\n\n<p>We are a team of data scientists and solar physicists that work together at developing new machine learning methods that support a deeper understanding of the mechanisms of solar activity in order to predict its events. The solar physics community is currently facing a deluge of data, which is too widely varied and complex to allow an overall analysis leading to a global understanding of solar activity. We propose to solve this problem by developing new machine learning algorithms that exploit these heterogeneous data, to:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>study the properties in 3D of objects of the solar atmosphere (filaments, sunspots\u2026),<\/li>\n\n\n\n<li>model their evolutions and behaviors,<\/li>\n\n\n\n<li>study the correlations between many indicators of solar activity (inc. solar objects and their behaviors), solar activity events (flares, CMEs&#8230;), and their resulting terrestrial impacts (geomagnetic indices&#8230;), and<\/li>\n\n\n\n<li>use these new insights to predict the events of solar activity and their effects on Earth.<\/li>\n<\/ol>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/presage.lis-lab.fr\/wp-content\/uploads\/2022\/12\/image-1.png\" alt=\"\" class=\"wp-image-31\" width=\"163\" height=\"169\"\/><\/figure>\n<\/div>\n\n\n<p>We are based at <a href=\"https:\/\/www.lis-lab.fr\/\">LIS<\/a> in the <a href=\"https:\/\/dyni.pages.lis-lab.fr\/\">DYNI team<\/a> (<a href=\"https:\/\/www.univ-tln.fr\/\">Universit\u00e9 de Toulon<\/a>) and <a href=\"https:\/\/lesia.obspm.fr\/\">LESIA<\/a> (<a href=\"https:\/\/www.observatoiredeparis.psl.eu\/\">Paris Observatory<\/a>).<\/p>\n\n\n\n<p>The project is funded by an ANR JCJC grant ANR-20-CE23-0014 for 4 years from September 2021. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Welcome to the PRESAGE project!PRESAGE stands for PREdicting Solar Activity using machine learning on heteroGEneous data. We are a team of data scientists and solar physicists that work together at developing new machine learning methods that support a deeper understanding of the mechanisms of solar activity in order to predict its events. The solar physics [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry","entry"],"_links":{"self":[{"href":"https:\/\/presage.lis-lab.fr\/index.php?rest_route=\/wp\/v2\/pages\/2","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/presage.lis-lab.fr\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/presage.lis-lab.fr\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/presage.lis-lab.fr\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/presage.lis-lab.fr\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2"}],"version-history":[{"count":6,"href":"https:\/\/presage.lis-lab.fr\/index.php?rest_route=\/wp\/v2\/pages\/2\/revisions"}],"predecessor-version":[{"id":32,"href":"https:\/\/presage.lis-lab.fr\/index.php?rest_route=\/wp\/v2\/pages\/2\/revisions\/32"}],"wp:attachment":[{"href":"https:\/\/presage.lis-lab.fr\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}