Anima Anandkumar, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Nikola Kovachki, Zongyi Li(李宗宜), Burigede Liu, and Andrew Stuart, Neural Operator: Graph Kernel Network for Partial Differential Equations, at ICLR Workshop on Integration of Deep Neural Models and Differential Equations (DeepDiffEq...
愿论文:Neural Operator: Graph Kernel Network for Partial Differential Equations 原作者英文blog:Graph Neural Operator for PDEs 引言 科学计算的成本非常高。数值求解器模拟流体动力学和多体运动可能需要几天甚至几个月。之所以如此,是因为为了获得良好的准确性,数值求解器需要将空间和时间划分成非常细小的网格,并在...
Graph neural networksGraph operator integratorSpace dependenceTime dependenceTRAFFIC FLOWLSTMFor multivariate time series forecasting problems, entirely using the dependencies between series is a crucial way to achieve accurate forecasting. Real-life multivariate time series often have complex time dependence, ...
The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces. The purpose of this work is to generalize neural networks so that they can learn mappings between infinit...
We combine the curvature-based graph neural network and AGN to propose the Curvature-based Adaptive Graph Neural Network (CurvAGN). We apply CurcAGN to predicting the protein-ligand binding affinity. We train and validate our model on the publicly available standard PDBbind-v2016 dataset, and sh...
Graph Neural Network Library for PyTorch. Contribute to pyg-team/pytorch_geometric development by creating an account on GitHub.
Recently, ECC was proposed by generalizing the convolution operator to graph and generate filter weights conditioned on edge attributes. Deep neural networks with ECC layers can handle datasets with varied graph sizes, apply graph convolutions to point clouds, and exploit the edge attributes. These ...
Graph Neural Architecture Search (GraphNAS for short) enables automatic design of the best graph neural architecture based on reinforcement learning. This directory contains code necessary to run GraphNAS. Specifically, GraphNAS first uses a recurrent network to generate variable-length strings that ...
Recently, graph neural networks (GNN) have shown strength in learning low-dimensional representations of individual cells by propagating neighbor cell features and constructing cell-cell relations in a global cell graph9,10. For example, our in-house tool scGNN, a GNN model, has demonstrated superi...
Deep convolutional neural networks (DCNNs) have enjoyed much success in many applications, such as computer vision, automated medical diagnosis, autonomous systems, etc. Another application of DCNNs is for game strategies, where the deep neural network architecture can be used to directly represent ...