Applied Mathematics and Computational Physics

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 th...

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Bibliographic Details
Other Authors: Wood, Aihua (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
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DOAB: description of the publication
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520 |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. 
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653 |a radial basis functions 
653 |a finite difference methods 
653 |a traveling waves 
653 |a non-uniform grids 
653 |a chaotic oscillator 
653 |a one-step method 
653 |a multi-step method 
653 |a computer arithmetic 
653 |a FPGA 
653 |a high strain rate impact 
653 |a modeling and simulation 
653 |a smoothed particle hydrodynamics 
653 |a finite element analysis 
653 |a hybrid nanofluid 
653 |a heat transfer 
653 |a non-isothermal 
653 |a shrinking surface 
653 |a MHD 
653 |a radiation 
653 |a multilayer perceptrons 
653 |a quaternion neural networks 
653 |a metaheuristic optimization 
653 |a genetic algorithms 
653 |a micropolar fluid 
653 |a constricted channel 
653 |a MHD pulsatile flow 
653 |a strouhal number 
653 |a flow pulsation parameter 
653 |a multiple integral finite volume method 
653 |a finite difference method 
653 |a Rosenau-KdV 
653 |a conservation 
653 |a solvability 
653 |a convergence 
653 |a transmission electron microscopy (TEM) 
653 |a convolutional neural networks (CNN) 
653 |a anomaly detection 
653 |a principal component analysis (PCA) 
653 |a machine learning 
653 |a deep learning 
653 |a neural networks 
653 |a Gallium-Arsenide (GaAs) 
653 |a radiation-based flowmeter 
653 |a two-phase flow 
653 |a feature extraction 
653 |a artificial intelligence 
653 |a time domain 
653 |a Boltzmann equation 
653 |a collision integral 
653 |a convolutional neural network 
653 |a annular regime 
653 |a scale layer-independent 
653 |a petroleum pipeline 
653 |a volume fraction 
653 |a dual energy technique 
653 |a prescribed heat flux 
653 |a similarity solutions 
653 |a dual solutions 
653 |a stability analysis 
653 |a RBF-FD 
653 |a node sampling 
653 |a lebesgue constant 
653 |a complex regions 
653 |a finite-difference methods 
653 |a data assimilation 
653 |a model order reduction 
653 |a finite elements analysis 
653 |a high dimensional data 
653 |a welding 
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