Data-driven design of complex network structures to promote synchronization
We consider the problem of optimizing the interconnection graphs of complex networks to promote synchronization. When traditional optimization methods are inapplicable, due to uncertain or unknown node dynamics, we propose a data-driven approach leveraging datasets of relevant examples. We analyze two case studies, with linear and nonlinear node dynamics. First, we show how including node dynamics in the objective function makes the optimal graphs heterogeneous. Then, we compare various design strategies, finding that the best either utilize data samples close to a specific Pareto front or a combination of a neural network and a genetic algorithm, with statistically better performance than the best examples in the datasets.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
AI-driven Inverse Design of Band-Tunable Mechanical Metastructures for Tailored Vibration Mitigation
On-demand vibration mitigation in a mechanical system needs the suitable design of multiscale metastructures, involving complex unit cells. In this study, immersing in the world of patterns and examining the structural d…
Band GapEnhancing Materials Discovery with Valence Constrained Design in Generative Modeling
Diffusion-based deep generative models have emerged as powerful tools for inverse materials design. Yet, many existing approaches overlook essential chemical constraints such as oxidation state balance, which can lead to…
Task-driven Heterophilic Graph Structure Learning
Graph neural networks (GNNs) often struggle to learn discriminative node representations for heterophilic graphs, where connected nodes tend to have dissimilar labels and feature similarity provides weak structural cues.…
Graph structure learningDissecting Model Failures in Abdominal Aortic Aneurysm Segmentation through Explainability-Driven Analysis
Computed tomography image segmentation of complex abdominal aortic aneurysms (AAA) often fails because the models assign internal focus to irrelevant structures or do not focus on thin, low-contrast targets. Where the mo…
Image SegmentationGeometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning
Graph-structured data typically exhibits complex topological heterogeneity, making it difficult to model accurately within a single Riemannian manifold. While emerging mixed-curvature methods attempt to capture such dive…
Graph Representation Learning