具体场景下的神经网络对称化

用于混凝土侵彻中平面六分量应力张量场预测的物理耦合图神经算子

A physics-coupled graph neural operator for mid-plane six-component stress tensor field prediction in concrete penetration

提出PC-GNO模型，基于骨料解析有限元数据训练，在函数空间预测混凝土侵彻中平面六分量应力张量场，精度优于傅里叶神经算子和线性回归，为该场景场级响应预测提供数据驱动路径。

Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings

We propose a framework that lifts the capabilities of graph convolutional networks (GCNs) to scenarios where no input graph is given and increases their robustness to adversarial attacks. We formulate a joint probabilistic model that considers a prior distribution over graphs along with a GCN-based likelihood and develop a stochastic variational inference algorithm to estimate the graph posterior and the GCN parameters jointly. To address the problem of propagating gradients through latent variables drawn from discrete distributions, we use their continuous relaxations known as Concrete distri…