Hi, I think the nn.Conv1d and nn.Linear are the same for pointwise convolution( ifkernel_size = 1for conv1d), so could you please provide some reasons about why you choose nn.Conv1d for the first three layers an
Figure 3.3 shows an interface between coarse (Δxc) and fine (Δxf=Δxc/2) patches of a 1D FDTD grid, on which for every time step Δtc on the coarse grid two time steps Δtc/2 are applied to advance solution on the fine grid. Solution on the coarse grid can be advanced in ...
It is well known that the Allen-Cahn equation satisfies the maximum bound principle and the energy dissipation law. Such two properties are important in the study of the stability of the solution to the Allen-Cahn equation, and whether they could be inherited at the discrete level is a signif...
Chapter 3: Introduction to the Finite-Difference Time-Domain Method: FDTD in 1D.This is where...
We define the mesh function {vk∣0≤k≤N} which is approximating v(t) and time tk vk=v(tk),0≤k≤N.It is straightforward to verify the equivalence between our method of computing the Caputo TFD and the following formula 0FHDtα,λv(t)∣t=tk+σ=∑i=1NexpwīVhist,i(tk)+μ...
The vanilla feed-forward network consists of two linear transformation layers, where the hidden dimension D′ between two layers is expanded to learn a richer feature representation. In contrast, we intro- duce a depthwise 3D convolution (with BN and ...
Figure 3.3 shows an interface between coarse (Δxc) and fine (Δxf=Δxc/2) patches of a 1D FDTD grid, on which for every time step Δtc on the coarse grid two time steps Δtc/2 are applied to advance solution on the fine grid. Solution on the coarse grid can be advanced in ...
However, previous approaches fail to capture the implicit correlations between joints and handle actions across varying time intervals. To address these problems, we propose an adaptive multi-scale difference graph convolution Network (AMD-GCN), which comprises an adaptive spatial graph convolution ...