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Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics / Felix Fritzen, David Ryckelynck
| Machine Learning, Low-Rank Approximations and Reduced Order Modeling in Computational Mechanics / Felix Fritzen, David Ryckelynck |
| Autore | Fritzen Felix |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2019 |
| Descrizione fisica | 1 electronic resource (254 p.) |
| Soggetto topico | History of engineering and technology |
| Soggetto non controllato |
supervised machine learning
proper orthogonal decomposition (POD) PGD compression stabilization nonlinear reduced order model gappy POD symplectic model order reduction neural network snapshot proper orthogonal decomposition 3D reconstruction microstructure property linkage nonlinear material behaviour proper orthogonal decomposition reduced basis ECSW geometric nonlinearity POD model order reduction elasto-viscoplasticity sampling surrogate modeling model reduction enhanced POD archive modal analysis low-rank approximation computational homogenization artificial neural networks unsupervised machine learning large strain reduced-order model proper generalised decomposition (PGD) a priori enrichment elastoviscoplastic behavior error indicator computational homogenisation empirical cubature method nonlinear structural mechanics reduced integration domain model order reduction (MOR) structure preservation of symplecticity heterogeneous data reduced order modeling (ROM) parameter-dependent model data science Hencky strain dynamic extrapolation tensor-train decomposition hyper-reduction empirical cubature randomised SVD machine learning inverse problem plasticity proper symplectic decomposition (PSD) finite deformation Hamiltonian system DEIM GNAT |
| ISBN |
9783039214105
3039214101 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910367759403321 |
Fritzen Felix
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| MDPI - Multidisciplinary Digital Publishing Institute, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Systems Analytics and Integration of Big Omics Data
| Systems Analytics and Integration of Big Omics Data |
| Autore | Hardiman Gary |
| Pubbl/distr/stampa | MDPI - Multidisciplinary Digital Publishing Institute, 2020 |
| Descrizione fisica | 1 online resource (202 p.) |
| Soggetto topico | Medicine |
| Soggetto non controllato |
algorithm development for network integration
Alzheimer's disease amyloid-beta annotation artificial intelligence biocuration bioinformatics pipelines candidate genes causal inference cell lines challenges chromatin modification class imbalance clinical data cognitive impairment curse of dimensionality data integration database deep phenotype dementia direct effect disease variants distance correlation drug sensitivity enrichment analysis epidemiological data epigenetics feature selection Gene Ontology gene-environment interactions genomics genotype heterogeneous data indirect effect integrative analytics joint modeling KEGG pathways logic forest machine learning microtubule-associated protein tau miRNA-gene expression networks missing data multi-omics multiomics integration multivariate analysis multivariate causal mediation n/a network topology analysis neurodegeneration non-omics data omics data pharmacogenomics phenomics phenotype plot visualization precision medicine informatics proteomic analysis regulatory genomics RNA expression scalability sequencing support vector machine systemic lupus erythematosus tissue classification tissue-specific expressed genes transcriptome |
| ISBN | 3-03928-745-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910404089603321 |
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Hardiman Gary
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| MDPI - Multidisciplinary Digital Publishing Institute, 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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