LEADER 00910nam0-22002771i-450 001 990004768330403321 005 20210922124403.0 035 $a000476833 100 $a19990530g19519999km-y0itay50------ba 101 0 $ager 105 $ay-------001yy 200 1 $aAndria$fP. Terentius Afer$etextbearbeitung, Einleitung und Eigennamenverzeichnis von Andreas Thierfelder 210 $aHeidelberg$cF.H. Kerl$d1951 215 $a2 v.$d21 cm 225 1 $aHeidelberger Texte$iLateinische Reihe$v22-22a 327 $a1.: Texte$a2.: Glossar zu P. Terentius Afer Andria 700 1$aTerentius Afer,$bPublius$f$0152516 702 1$aThierfelder,$bAndreas 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004768330403321 952 $aVI D 10 (1)$bBibl. 24971$fFLFBC 952 $aVI D 10 (2)$bBibl. 24971$fFLFBC 959 $aFLFBC 996 $aAndria$920081 997 $aUNINA LEADER 01086nam a2200301 i 4500 001 991003221309707536 005 20020509114318.0 008 950926s1993 it ||| | ita 020 $a8876923659 035 $ab11127764-39ule_inst 035 $aPARLA177564$9ExL 040 $aDip.to Filol. 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In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm. 606 $aTechnology: general issues$2bicssc 610 $aadditive manufacturing 610 $aanalytical model 610 $aartificial neural networks 610 $aCode2Vect 610 $acomputational modeling 610 $aconstitutive modeling 610 $adata driven 610 $adata mining 610 $adata-driven 610 $adata-driven mechanics 610 $aeffective properties 610 $aelasto-visco-plasticity 610 $aFE-beam model 610 $afeature engineering 610 $afinite element model 610 $aGaussian process 610 $aGaussian process regression 610 $aGENERIC 610 $ahardness 610 $ahigh-throughput experimentation 610 $ahyperelasticity 610 $alaser shock peening 610 $amachine learning 610 $amanifold learning 610 $amechanical properties 610 $amicrocompression 610 $amicrostructures 610 $amodel calibration 610 $amodel correction 610 $amultiscale 610 $an/a 610 $ananoindentation 610 $ananoporous metals 610 $aneural networks 610 $anonlinear 610 $anonlinear regression 610 $aopen-pore foams 610 $aphysics based 610 $aplasticity 610 $aprincipal component analysis 610 $aresidual stresses 610 $asensitivity analysis 610 $asoft living tissues 610 $aspherical indentation 610 $astatistical analysis 610 $astochastics 610 $astructure-property relationship 610 $aTDA 610 $aTi-Mn alloys 610 $atopological data analysis 615 7$aTechnology: general issues 700 $aChinesta$b Francisco$4edt$0720584 702 $aCueto$b Eli?as$4edt 702 $aKlusemann$b Benjamin$4edt 702 $aChinesta$b Francisco$4oth 702 $aCueto$b Eli?as$4oth 702 $aKlusemann$b Benjamin$4oth 906 $aBOOK 912 $a9910557717703321 996 $aEmpowering Materials Processing and Performance from Data and AI$93035953 997 $aUNINA