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220822 ||| eng |
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|a books978-3-0365-2306-4
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| 020 |
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|a 9783036523064
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|a 9783036523057
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| 100 |
1 |
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|a Wood, Aihua
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| 245 |
0 |
0 |
|a Applied Mathematics and Computational Physics
|h Elektronische Ressource
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| 260 |
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|a Basel, Switzerland
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2021
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| 300 |
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|a 1 electronic resource (273 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a stability analysis
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| 653 |
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|a radial basis functions
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| 653 |
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|a RBF-FD
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| 653 |
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|a high dimensional data
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| 653 |
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|a multi-step method
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| 653 |
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|a non-isothermal
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| 653 |
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|a multiple integral finite volume method
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| 653 |
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|a two-phase flow
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| 653 |
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|a petroleum pipeline
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| 653 |
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|a chaotic oscillator
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| 653 |
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|a conservation
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| 653 |
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|a Mathematics & science / bicssc
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| 653 |
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|a Boltzmann equation
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| 653 |
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|a FPGA
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| 653 |
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|a MHD
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| 653 |
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|a finite difference method
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| 653 |
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|a constricted channel
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| 653 |
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|a complex regions
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| 653 |
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|a multilayer perceptrons
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| 653 |
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|a flow pulsation parameter
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| 653 |
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|a principal component analysis (PCA)
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| 653 |
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|a model order reduction
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| 653 |
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|a lebesgue constant
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| 653 |
|
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|a computer arithmetic
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| 653 |
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|a radiation
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| 653 |
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|a deep learning
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| 653 |
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|a time domain
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| 653 |
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|a prescribed heat flux
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| 653 |
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|a neural networks
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| 653 |
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|a welding
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| 653 |
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|a non-uniform grids
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| 653 |
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|a MHD pulsatile flow
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| 653 |
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|a strouhal number
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| 653 |
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|a micropolar fluid
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| 653 |
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|a scale layer-independent
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| 653 |
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|a node sampling
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| 653 |
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|a data assimilation
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| 653 |
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|a hybrid nanofluid
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| 653 |
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|a feature extraction
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| 653 |
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|a heat transfer
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| 653 |
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|a dual energy technique
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| 653 |
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|a annular regime
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| 653 |
|
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|a Gallium-Arsenide (GaAs)
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| 653 |
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|a dual solutions
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| 653 |
|
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|a Research & information: general / bicssc
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| 653 |
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|a radiation-based flowmeter
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| 653 |
|
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|a quaternion neural networks
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| 653 |
|
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|a convergence
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| 653 |
|
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|a finite difference methods
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| 653 |
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|a Rosenau-KdV
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| 653 |
|
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|a genetic algorithms
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| 653 |
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|a shrinking surface
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| 653 |
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|a finite-difference methods
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| 653 |
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|a collision integral
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| 653 |
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|a smoothed particle hydrodynamics
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| 653 |
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|a solvability
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| 653 |
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|a finite element analysis
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| 653 |
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|a artificial intelligence
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| 653 |
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|a volume fraction
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| 653 |
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|a transmission electron microscopy (TEM)
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| 653 |
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|a similarity solutions
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| 653 |
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|a modeling and simulation
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| 653 |
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|a anomaly detection
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| 653 |
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|a convolutional neural network
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| 653 |
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|a one-step method
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| 653 |
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|a metaheuristic optimization
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| 653 |
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|a high strain rate impact
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| 653 |
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|a traveling waves
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| 653 |
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|a convolutional neural networks (CNN)
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| 653 |
|
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|a finite elements analysis
|
| 700 |
1 |
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|a Wood, Aihua
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b DOAB
|a Directory of Open Access Books
|
| 500 |
|
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
5 |
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|a 10.3390/books978-3-0365-2306-4
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| 856 |
4 |
0 |
|u https://www.mdpi.com/books/pdfview/book/4534
|7 0
|x Verlag
|3 Volltext
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| 856 |
4 |
2 |
|u https://directory.doabooks.org/handle/20.500.12854/76943
|z DOAB: description of the publication
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| 082 |
0 |
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|a 000
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| 082 |
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|a 333
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| 082 |
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|a 500
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|a 700
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| 520 |
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|a As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications.
|