论文标题
部分可观测时空混沌系统的无模型预测
Inverse Boundary Value and Optimal Control Problems on Graphs: A Neural and Numerical Synthesis
论文作者
论文摘要
引入了Dirichlet和Neumann边界条件的图表上确定性系统识别问题的一般设置。当控制节点沿边界可用时，我们将使用离散化 - 然后优化方法来估计最佳控制。当前体系结构中的一个关键部分是我们通过神经网络的边界注入消息。这将产生更准确的预测，这些预测在边界的接近度中更加稳定。同样，引入了基于图形距离的正则化技术，该技术有助于稳定远离边界的节点的预测。
A general setup for deterministic system identification problems on graphs with Dirichlet and Neumann boundary conditions is introduced. When control nodes are available along the boundary, we apply a discretize-then-optimize method to estimate an optimal control. A key piece in the present architecture is our boundary injected message passing neural network. This will produce more accurate predictions that are considerably more stable in proximity of the boundary. Also, a regularization technique based on graphical distance is introduced that helps with stabilizing the predictions at nodes far from the boundary.
