Results 271 to 280 of about 35,975,054 (321)
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Hybrid Difference Methods for PDEs
Journal of Scientific Computing, 2014The author introduces a numerical method for partial differential equations (PDEs) which consists of two steps: the finite difference approximation of PDEs within cells and the interface finite difference on edges of cells. As the author explains in the paper, the method is suitable to non-uniform grids, retaining the optimal order of convergence. Some
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1998
To solve the boundary value problems of elliptic equations, especially with singularities and unbounded domains, the simplified hybrid combinations are presented in this chapter, which also adopt the solution flux on the side ∂S2 of S2 where the Ritz-Galerkin method applies.
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To solve the boundary value problems of elliptic equations, especially with singularities and unbounded domains, the simplified hybrid combinations are presented in this chapter, which also adopt the solution flux on the side ∂S2 of S2 where the Ritz-Galerkin method applies.
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2002
In Chapters 4 and 5 the connectionist, multi-layer architectures of fuzzy systems, called fuzzy inference neural networks, were presented. These architectures are similar to neural networks (see Section 3.1), so learning algorithms can be proposed to tune the parameters of the networks, analo gously to tuning weights in neural networks.
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In Chapters 4 and 5 the connectionist, multi-layer architectures of fuzzy systems, called fuzzy inference neural networks, were presented. These architectures are similar to neural networks (see Section 3.1), so learning algorithms can be proposed to tune the parameters of the networks, analo gously to tuning weights in neural networks.
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2012
The chapter focuses upon the use of hybrid planning systems that are primarily behavioral in nature. Three approaches are discussed. In the first approach, the authors solve the problem of path planning using a combination of A* algorithm and Fuzzy Inference System. The A* algorithm does the higher-level planning by working on a lower detail map.
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The chapter focuses upon the use of hybrid planning systems that are primarily behavioral in nature. Three approaches are discussed. In the first approach, the authors solve the problem of path planning using a combination of A* algorithm and Fuzzy Inference System. The A* algorithm does the higher-level planning by working on a lower detail map.
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Mixed and Hybrid Finite Element Methods
Springer Series in Computational Mathematics, 2011F. Brezzi, M. Fortin
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Modified two‐step hybrid methods for the numerical integration of oscillatory problems
, 2017T. Monovasilis +3 more
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Epitaxial growth of hybrid nanostructures
Nature Reviews Materials, 2018Chaoliang Tan, Junze Chen, Xue-Jun Wu
exaly