<manifestation xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:tucdl="http://purl.tuc.gr/dl/dias/schemas/aip/tucdl/" xmlns="http://purl.tuc.gr/dl/dias/schemas/aip/tucdl/" keyIdentifier="http://purl.tuc.gr/dl/dias/0FD435F3-F218-4219-A089-39A3F5149096" xsi:schemaLocation="http://purl.tuc.gr/dl/dias/schemas/aip/tucdl/ http://purl.tuc.gr/dl/dias/schemas/aip/tucdl"><titleOfTheManifestation>Stavroulakis_et_al_Appl. Sci._12(23)_2022.pdf</titleOfTheManifestation><isEmbodimentOf entityType="Expression"><uri>http://purl.tuc.gr/dl/dias/303838EF-62DD-4BE7-8E89-4634758A23A7</uri><title xml:lang="en">Review of computational mechanics, optimization, and machine learning tools for digital twins applied to infrastructures</title></isEmbodimentOf><accessRestrictionOnTheManifestation>free</accessRestrictionOnTheManifestation><dateOfPublicationDistribution>2023-08-25</dateOfPublicationDistribution><formOfCarrier>application/pdf</formOfCarrier><extentOfTheCarrier xml:lang="en">1.3 MB</extentOfTheCarrier></manifestation>