May 1, 2024 · Our work introduces a leap forward in scientific computing and CEM by proposing an original solution of Maxwell's equations that is grounded on graph neural ...
May 1, 2024 · Updating the discretized Maxwell equations is equivalent to a two-step message-passing process between the network neurons. During the first ...
Mar 17, 2023 · Kuhn et al. developed a graph neural network to solve Maxwell's equations numerically. Graph neural networks are useful for processing unstructured and non- ...
Missing: Message Passing.
Mar 14, 2023 · Having an artificial neural network that solves Maxwell's equations in a general setting is an intellectual challenge and a great utility.
本篇论文旨在通过提出一种基于图神经网络（GNN）的方法，解决计算电磁学（CEM）中Maxwell方程的数值求解问题。现有的CEM方法计算成本高，本论文尝试提出一种更高效的解决方案。
Oct 22, 2024 · Having an artificial neural network that solves Maxwell's equations in a general setting is an intellectual challenge and a great utility.
2.3.3 Message Passing in Graph Neural Differential Equations. Let's delve into the workings of message passing in Graph NDEs. For a given node, rep- resented ...
Jan 22, 2024 · We present a novel method, termed GMFGRN, for accurate graph neural network (GNN)-based GRN inference from scRNA-seq data.
This study links the FDTD method with the artificial neural network such that some electromagnetic problems can be solved on the deep learning framework and ...
The rapid development of neural network (NN) methods for solving partial differential equations. (PDEs) has created an urgent need for evaluation.