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Applied Mathematics and Computational Physics
| Applied Mathematics and Computational Physics |
| Autore | Wood Aihua |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 online resource (273 p.) |
| Soggetto topico |
Mathematics & science
Research & information: general |
| Soggetto non controllato |
annular regime
anomaly detection artificial intelligence Boltzmann equation chaotic oscillator collision integral complex regions computer arithmetic conservation constricted channel convergence convolutional neural network convolutional neural networks (CNN) data assimilation deep learning dual energy technique dual solutions feature extraction finite difference method finite difference methods finite element analysis finite elements analysis finite-difference methods flow pulsation parameter FPGA Gallium-Arsenide (GaAs) genetic algorithms heat transfer high dimensional data high strain rate impact hybrid nanofluid lebesgue constant machine learning metaheuristic optimization MHD MHD pulsatile flow micropolar fluid model order reduction modeling and simulation multi-step method multilayer perceptrons multiple integral finite volume method neural networks node sampling non-isothermal non-uniform grids one-step method petroleum pipeline prescribed heat flux principal component analysis (PCA) quaternion neural networks radial basis functions radiation radiation-based flowmeter RBF-FD Rosenau-KdV scale layer-independent shrinking surface similarity solutions smoothed particle hydrodynamics solvability stability analysis strouhal number time domain transmission electron microscopy (TEM) traveling waves two-phase flow volume fraction welding |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557337303321 |
Wood Aihua
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Evolutionary Algorithms in Engineering Design Optimization
| Evolutionary Algorithms in Engineering Design Optimization |
| Autore | Greiner David |
| Pubbl/distr/stampa | Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 |
| Descrizione fisica | 1 online resource (314 p.) |
| Soggetto topico |
History of engineering & technology
Technology: general issues |
| Soggetto non controllato |
accuracy levels
aeroacoustics archiving strategy artificial neural networks (ANN) limited training data Automatic Voltage Regulation system availability bankruptcy problem beam improvements beam T-junctions models Chaotic optimization classification control design differential evolution distance-based diversity control encoding evolutionary algorithm evolutionary algorithms evolutionary optimization experimental study finite elements analysis Fractional Order Proportional-Integral-Derivative controller genetic algorithm genetic programming global optimisation global optimization Gough-Stewart launchers machine learning machine vision min-max optimization mono and multi-objective optimization multi-objective decision-making multi-objective evolutionary algorithms multi-objective optimisation multi-objective optimization mutation-selection nearly optimal solutions neural networks non-linear parametric identification optimal control optimal design parallel manipulator parameter optimization Pareto front performance metrics plastics thermoforming preference in multi-objective optimization preventive maintenance scheduling quality control real application reusable launch vehicle robust robust design roughness measurement sheet thickness distribution space systems spaceplanes surrogate T-junctions trailing-edge noise trajectory optimisation two-stage method uncertainty quantification unscented transformation worst-case scenario Yellow Saddle Goatfish Algorithm |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910566486903321 |
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Greiner David
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| Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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