{"id":9838,"date":"2026-01-19T07:02:30","date_gmt":"2026-01-19T07:02:30","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2026\/01\/19\/2504-04480\/"},"modified":"2026-01-19T07:02:30","modified_gmt":"2026-01-19T07:02:30","slug":"2504-04480","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2026\/01\/19\/2504-04480\/","title":{"rendered":"Fine Tuning a Simulation-Driven Estimator"},"content":{"rendered":"<p>    Fine Tuning a Simulation-Driven Estimator<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>arXiv:2504.04480v2 Announce Type: cross<br \/>\nAbstract: Many industries now deploy high-fidelity simulators (digital twins) to represent physical systems, yet their parameters must be calibrated to match the true system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping from observations to parameters. However, when the true parameters lie outside the sampled range, predictions suffer from an out-of-distribution (OOD) error. This paper introduces a fine-tuning approach for the Two-Stage estimator that mitigates OOD effects and improves accuracy. The effectiveness of the proposed method is verified through numerical simulations.<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Braghadeesh Lakshminarayanan, Margarita A. Guerrero, Cristian R. Rojas<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/arxiv.org\/abs\/2504.04480\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Fine Tuning a Simulation-Driven Estimator arXiv:2504.04480v2 Announce Type: cross Abstract: Many industries now deploy high-fidelity simulators (digital twins) to represent physical systems, yet their parameters must be calibrated to match the true system. This motivated the construction of simulation-driven parameter estimators, built by generating synthetic observations for sampled parameter values and learning a supervised mapping [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,493,494,112],"tags":[135,101,1113],"class_list":["post-9838","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-cs-sy","category-eess-sy","category-stat-ml","tag-fine","tag-simulation","tag-tuning"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9838"}],"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=9838"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/9838\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=9838"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=9838"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=9838"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}