Empowering Materials Processing and Performance from Data and AI
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 mat...
Αποθηκεύτηκε σε:
| Μορφή: | Online |
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| Γλώσσα: | Αγγλικά |
| Έκδοση: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Θέματα: | |
| Διαθέσιμο Online: | ONIX_20220111_9783036518992_594 |
| Ετικέτες: |
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| _version_ | 1869515147799691264 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-76859 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-768592024-04-09T23:16:21Z Empowering Materials Processing and Performance from Data and AI Chinesta, Francisco Cueto, Elías Klusemann, Benjamin plasticity machine learning constitutive modeling manifold learning topological data analysis GENERIC soft living tissues hyperelasticity computational modeling data-driven mechanics TDA Code2Vect nonlinear regression effective properties microstructures model calibration sensitivity analysis elasto-visco-plasticity Gaussian process high-throughput experimentation additive manufacturing Ti–Mn alloys spherical indentation statistical analysis Gaussian process regression nanoporous metals open-pore foams FE-beam model data mining mechanical properties hardness principal component analysis structure–property relationship microcompression nanoindentation analytical model finite element model artificial neural networks model correction feature engineering physics based data driven laser shock peening residual stresses data-driven multiscale nonlinear stochastics neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues 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. 2022-01-11T13:44:22Z 2022-01-11T13:44:22Z 2021 book ONIX_20220111_9783036518992_594 9783036518992 9783036518985 https://directory.doabooks.org/handle/20.500.12854/76859 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/4327 https://mdpi.com/books/pdfview/book/4327 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-1898-5 10.3390/books978-3-0365-1898-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036518992 9783036518985 156 Basel, Switzerland open access |
| spellingShingle | plasticity machine learning constitutive modeling manifold learning topological data analysis GENERIC soft living tissues hyperelasticity computational modeling data-driven mechanics TDA Code2Vect nonlinear regression effective properties microstructures model calibration sensitivity analysis elasto-visco-plasticity Gaussian process high-throughput experimentation additive manufacturing Ti–Mn alloys spherical indentation statistical analysis Gaussian process regression nanoporous metals open-pore foams FE-beam model data mining mechanical properties hardness principal component analysis structure–property relationship microcompression nanoindentation analytical model finite element model artificial neural networks model correction feature engineering physics based data driven laser shock peening residual stresses data-driven multiscale nonlinear stochastics neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Empowering Materials Processing and Performance from Data and AI |
| title | Empowering Materials Processing and Performance from Data and AI |
| title_full | Empowering Materials Processing and Performance from Data and AI |
| title_fullStr | Empowering Materials Processing and Performance from Data and AI |
| title_full_unstemmed | Empowering Materials Processing and Performance from Data and AI |
| title_short | Empowering Materials Processing and Performance from Data and AI |
| title_sort | empowering materials processing and performance from data and ai |
| topic | plasticity machine learning constitutive modeling manifold learning topological data analysis GENERIC soft living tissues hyperelasticity computational modeling data-driven mechanics TDA Code2Vect nonlinear regression effective properties microstructures model calibration sensitivity analysis elasto-visco-plasticity Gaussian process high-throughput experimentation additive manufacturing Ti–Mn alloys spherical indentation statistical analysis Gaussian process regression nanoporous metals open-pore foams FE-beam model data mining mechanical properties hardness principal component analysis structure–property relationship microcompression nanoindentation analytical model finite element model artificial neural networks model correction feature engineering physics based data driven laser shock peening residual stresses data-driven multiscale nonlinear stochastics neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| topic_facet | plasticity machine learning constitutive modeling manifold learning topological data analysis GENERIC soft living tissues hyperelasticity computational modeling data-driven mechanics TDA Code2Vect nonlinear regression effective properties microstructures model calibration sensitivity analysis elasto-visco-plasticity Gaussian process high-throughput experimentation additive manufacturing Ti–Mn alloys spherical indentation statistical analysis Gaussian process regression nanoporous metals open-pore foams FE-beam model data mining mechanical properties hardness principal component analysis structure–property relationship microcompression nanoindentation analytical model finite element model artificial neural networks model correction feature engineering physics based data driven laser shock peening residual stresses data-driven multiscale nonlinear stochastics neural networks n/a thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| url | ONIX_20220111_9783036518992_594 |