For a suitable scaling of the solution to the one-dimensional heat equation with spatial-dependent coefficients and weakly dependent random initial conditions, the convergence to the Gaussian limiting distribution is proved. The scaling proposed and methodology followed allow us to obtain Gaussian ...
9.Relationship of Element Size and Precision With Gauss Heat Source高斯热源模型中单元尺寸与精度关系 10.Brain MR images skull stripping model based on multi-Gaussian mixture model多元高斯混合模型脑MR图像去壳模型 11.In 490 BC Phidippides ran to bring the news of victory over the Persians to athe...
The article considers the self-similar solution of the nonlinear heat-conduction equation with a three-dimensional source evolving under blow-up conditions... ES Kurkina,IM Nikol’Skii - 《Computational Mathematics & Modeling》 被引量: 6发表: 2006年 On the uniqueness for the heat equation with ...
Assuming a single point source in homogenous and stationary wind and turbulence intensity, the atmospheric transport equation (Eq. (1)) can be solved analytically yielding a normally distributed concentration field (Stockie, 2011). Gaussian (plume) models are based on this analytic result that provid...
The heat equation, Eq. (3.29), is the anisotropic diffusion equation: (3.32)∂P/∂t=∇⋅(cx,y(t)∇Px,y(t)) where ∇⋅ is the divergence operator (which essentially measures how the density within a region changes), with diffusion coefficient cx,y. The diffusion coefficient ...
A numerical procedure for an inverse problem of identification of an unknown source in a heat equation is presented. Approach of proposed method is to appr... AG Fatullayev - 《Mathematics & Computers in Simulation》 被引量: 103发表: 2002年 The radial basis functions method for identifying an...
1Citations Metricsdetails Abstract Monitoring water resources requires accurate predictions of rainfall data. Our study introduces a novel deep learning model named the deep residual shrinkage network (DRSN)—temporal convolutional network (TCN) to remove redundant features and extract temporal features from...
This covariance function can be derived in a few different ways: as the infinite limit of a radial basis function neural network, as diffusion in the heat equation, as a Gaussian filter in Fourier space or as the composition as a series of linear filters applied to a base function....
Gaussianfunction Normalized Gaussian curves with expected valueμ and varianceσ2. The corresponding parameters are a =
The heat equation and interface conditions (7) are linear, but the dependence on the environment is non-linear, as expected for a heterogeneous system. For any finite number of barriers using the Fourier or Laplace transform in the position space reduces the problem to solving a system of ...