{"version":"1.0","provider_name":"KMI - Nagoya University","provider_url":"https:\/\/www.kmi.nagoya-u.ac.jp\/eng","title":"Differentiable and symbolic modeling of the Universe in the age of machine learning - KMI - Nagoya University","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"IjxCuHQTTY\"><a href=\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/3243\/\">Differentiable and symbolic modeling of the Universe in the age of machine learning<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/3243\/embed\/#?secret=IjxCuHQTTY\" width=\"600\" height=\"338\" title=\"&#8220;Differentiable and symbolic modeling of the Universe in the age of machine learning&#8221; &#8212; KMI - Nagoya University\" data-secret=\"IjxCuHQTTY\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/* ]]> *\/\n<\/script>\n","thumbnail_url":"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/wp-content\/uploads\/sites\/2\/2024\/08\/20240827_kmi_seminar_yinli-scaled.jpg","thumbnail_width":1811,"thumbnail_height":2560,"description":"Date: August 27th (Tue) 13:30 &#8211; 14:30 Place: ES635 Speaker: Yin Li (Peng Cheng Laboratory) Abstract Rapid advances in machine learning have brought not only myriad powerful neural network models, but also breakthroughs that can potentially benefit established scientific research. This talk focuses on two such applications in cosmology. The first one aims at optimal inference of physical information from cosmological surveys, by differentiable forward modeling of the Universe. Using the adjoint method, we&#8217;ve developed a memory and computation efficient &hellip;"}