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We highlight two core use cases for this framework: (a) for generating a robust estimate of the systematic uncertainty in parameter reconstruction associated with the training procedure, and (b) for detecting possible model misspecification when using trained estimators on real data. We also demonstrate the relationship between significant KL divergences and issues such as insufficient convergence due to, e.g. too low a simulation budget, or intrinsic variance in the training process. Overall, this ensemble-based diagnostic framework provides a lightweight, scalable, and model-agnostic tool for enhancing the trustworthiness of SBI in scientific applications.<\/jats:p>","DOI":"10.1088\/2632-2153\/ae3103","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T22:49:42Z","timestamp":1766616582000},"page":"015008","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Simulation-based inference with deep ensembles: evaluating calibration uncertainty and detecting model misspecification"],"prefix":"10.1088","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2020-0803","authenticated-orcid":true,"given":"James","family":"Alvey","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7285-0707","authenticated-orcid":false,"given":"Carlo R","family":"Contaldi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0665-266X","authenticated-orcid":false,"given":"Mauro","family":"Pieroni","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2026,1,12]]},"reference":[{"key":"mlstae3103bib1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1912789117","type":"journal-article","volume":"117","author":"Cranmer","year":"2020","journal-title":"Proc. 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