{"id":3157,"date":"2025-04-17T07:02:32","date_gmt":"2025-04-17T07:02:32","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/04\/17\/2504-11516\/"},"modified":"2025-04-17T07:02:32","modified_gmt":"2025-04-17T07:02:32","slug":"2504-11516","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/04\/17\/2504-11516\/","title":{"rendered":"FEAT: Free energy Estimators with Adaptive Transport"},"content":{"rendered":"<p>    FEAT: Free energy Estimators with Adaptive Transport<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2504.11516v1 Announce Type: new<br \/>\nAbstract: We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation &#8212; a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled Crooks theorem, alongside variational upper and lower bounds on free energy differences. Unifying equilibrium and non-equilibrium methods under a single theoretical framework, FEAT establishes a principled foundation for neural free energy calculations. Experimental validation on toy examples, molecular simulations, and quantum field theory demonstrates improvements over existing learning-based methods.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Jiajun He, Yuanqi Du, Francisco Vargas, Yuanqing Wang, Carla P. Gomes, Jos&#8217;e Miguel Hern&#8217;andez-Lobato, Eric Vanden-Eijnden<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2504.11516\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>FEAT: Free energy Estimators with Adaptive Transport arXiv:2504.11516v1 Announce Type: new Abstract: We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation &#8212; a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,113,2395,377,112],"tags":[372,2396,25],"class_list":["post-3157","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-lg","category-physics-chem-ph","category-physics-comp-ph","category-stat-ml","tag-energy","tag-feat","tag-free"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/3157"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=3157"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/3157\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=3157"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=3157"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=3157"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}