4 papers
Brain-inspired Chaotic Graph Backpropagation for Large-scale Combinatorial Optimization
Peng Tao, Kazuyuki Aihara, Luonan Chen
Graph neural networks (GNNs) with unsupervised learning can solve large-scale combinatorial optimization problems (COPs) with efficient time complexity, making them versatile for v…
Deciphering interventional dynamical causality from non-intervention complex systems
Jifan Shi, Yang Li, Juan Zhao +5
Detecting and quantifying causality is a focal topic in the fields of science, engineering, and interdisciplinary studies. However, causal studies on non-intervention systems attra…
Designing Chaotic Attractors: A Semi-supervised Approach
Tempei Kabayama, Yasuo Kuniyoshi, Kazuyuki Aihara +1
Chaotic dynamics are ubiquitous in nature and useful in engineering, but their geometric design can be challenging. Here, we propose a method using reservoir computing to generate…
Predicting unobserved climate time series data at distant areas via spatial correlation using reservoir computing
Shihori Koyama, Daisuke Inoue, Hiroaki Yoshida +2
Collecting time series data spatially distributed in many locations is often important for analyzing climate change and its impacts on ecosystems. However, comprehensive spatial da…