Allen-Cahn Message Passing 我们提出了基于方程的Allen-Cahn消息传递(ACMP)神经网络,其中消息通过一个神经ODE求解器通过方程的演化进行更新。据我们所知,这是第一次引入一种信息传递类型,通过排斥力来放大连接节点之间的差异。 Network Architecture.假设d维矩阵x表示节点特征,其中第i行表示节点i的特征。首先对节点特征...
Here we propose a variational autoregressive architecture with a message passing mechanism, which effectively utilizes the interactions between spin variables. The architecture trained under an annealing framework outperforms existing neural network-based methods in solving several prototypical Ising spin ...
the parameters in the MPNN portion of the model will be specified from the path and frozen. Layers in the FFNN portion of the model can also be applied and frozen in addition to freezing the MPNN using--frzn_ffn_layers <number-of-layers>. Model architecture of the new model should match...
Model architecture of the new model should match the old model in any layers that are being frozen, but non-frozen layers can be different without affecting the frozen layers (e.g., MPNN alone is frozen and new model has a larger number of FFNN layers). Parameters provided with --...
Because of complex local geometry, the "image-like" data representations required for implementing standard convolutional neural network based metamodels are not ideal, thus motivating the use of GNNs. In addition to investigating GNN model architecture, we study the effect of different input data ...
Attention message passing neural network (AMPNN) Here we propose a further augmentation to the MPNN architecture by considering a more general form of the MPNN message summation step (Eq.1). Using simple summation to convert an unknown cardinality set of vectors into a single vector is hypothetic...
Architecture Diverse forms of force fields are manifestly responsible for the intricate interactions, especially in systems with multiple elements. GNNs for homogeneous graphs model interactions of different atomic pairs with shared parameters, which limits the expressive power for neural-network-based force...
Graph Neural Networks (GNNs) have emerged as a promising approach for improving the accuracy and speed of PF approximations by exploiting information sharing via the underlying graph structure. In this study, we introduce PowerFlowNet, a novel GNN architecture for PF approximation that showcases simil...
Since we now introduce vector features as one of the attributes of atoms, we thereby enforce the model to remain equivariant to the rotations in the 3D coordinate space and preserve this feature throughout the network. By infusing more physical priors into the network architecture, NewtonNet ...
Although the default message passing architecture works quite well on a variety of datasets, optimizing the hyperparameters for a particular dataset often leads to marked improvement in predictive performance. We have automated hyperparameter optimization via Bayesian optimization (using thehyperoptpackage)...