04204nam 2200961z- 450 991055771770332120231214133059.0(CKB)5400000000046148(oapen)https://directory.doabooks.org/handle/20.500.12854/76859(EXLCZ)99540000000004614820202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierEmpowering Materials Processing and Performance from Data and AIBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic resource (156 p.)3-0365-1899-1 3-0365-1898-3 Third 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.Technology: general issuesbicsscplasticitymachine learningconstitutive modelingmanifold learningtopological data analysisGENERICsoft living tissueshyperelasticitycomputational modelingdata-driven mechanicsTDACode2Vectnonlinear regressioneffective propertiesmicrostructuresmodel calibrationsensitivity analysiselasto-visco-plasticityGaussian processhigh-throughput experimentationadditive manufacturingTi-Mn alloysspherical indentationstatistical analysisGaussian process regressionnanoporous metalsopen-pore foamsFE-beam modeldata miningmechanical propertieshardnessprincipal component analysisstructure-property relationshipmicrocompressionnanoindentationanalytical modelfinite element modelartificial neural networksmodel correctionfeature engineeringphysics baseddata drivenlaser shock peeningresidual stressesdata-drivenmultiscalenonlinearstochasticsneural networksTechnology: general issuesChinesta Franciscoedt720584Cueto ElíasedtKlusemann BenjaminedtChinesta FranciscoothCueto ElíasothKlusemann BenjaminothBOOK9910557717703321Empowering Materials Processing and Performance from Data and AI3035953UNINA