LEADER 04204nam 2200961z- 450 001 9910557717703321 005 20231214133059.0 035 $a(CKB)5400000000046148 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76859 035 $a(EXLCZ)995400000000046148 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEmpowering Materials Processing and Performance from Data and AI 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (156 p.) 311 $a3-0365-1899-1 311 $a3-0365-1898-3 330 $aThird millennium engineering address new challenges in materials sciences and engineering. 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 $aplasticity 610 $amachine learning 610 $aconstitutive modeling 610 $amanifold learning 610 $atopological data analysis 610 $aGENERIC 610 $asoft living tissues 610 $ahyperelasticity 610 $acomputational modeling 610 $adata-driven mechanics 610 $aTDA 610 $aCode2Vect 610 $anonlinear regression 610 $aeffective properties 610 $amicrostructures 610 $amodel calibration 610 $asensitivity analysis 610 $aelasto-visco-plasticity 610 $aGaussian process 610 $ahigh-throughput experimentation 610 $aadditive manufacturing 610 $aTi-Mn alloys 610 $aspherical indentation 610 $astatistical analysis 610 $aGaussian process regression 610 $ananoporous metals 610 $aopen-pore foams 610 $aFE-beam model 610 $adata mining 610 $amechanical properties 610 $ahardness 610 $aprincipal component analysis 610 $astructure-property relationship 610 $amicrocompression 610 $ananoindentation 610 $aanalytical model 610 $afinite element model 610 $aartificial neural networks 610 $amodel correction 610 $afeature engineering 610 $aphysics based 610 $adata driven 610 $alaser shock peening 610 $aresidual stresses 610 $adata-driven 610 $amultiscale 610 $anonlinear 610 $astochastics 610 $aneural networks 615 7$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