6 papers
DisKO: Deep Koopman Learning in Distribution Space from Unpaired Snapshots
He Ma, Xiaochen Liu, Wanfeng Lu +3
Many complex systems are observed only through temporally unpaired distribution snapshots, making trajectory-based dynamical learning difficult without additional assumptions. We t…
Event-Triggered Stabilisation of Desynchronisation in Networked Oscillatory Systems
Luan Yang, Jingdong Zhang, Jeroen Lamb +3
Pathological neuronal synchrony provides one practical motivation for studying sparse desynchronisation control in networked oscillatory systems, particularly in applications where…
Towards transferable lightweight neuromorphic computing through a model-free temporal-switch framework
Zefeng Zhang, Chao Li, Siyao Chen +5
Lightweight neuromorphic computing offers a promising route to efficient AI, with particular benefits for resource-constrained edge deployments. However, its scalable deployment th…
Ptolemy's Equant Equates to a Universal Dynamical Clock via Machine Learning
Jingdong Zhang, Luan Yang, Murilo S. Baptista +4
Oscillatory dynamics arise ubiquitously in nonlinear systems, yet identifying a physically interpretable phase and phase dynamics in nonlinear, high-dimensional oscillations remain…
Predicting Dynamics of Ultra-Large Complex Systems by Inferring Governing Equations
Qi Shao, Duxin Chen, Jiawen Chen +5
Predicting the behavior of ultra-large complex systems, from climate to biological and technological networks, is a central unsolved challenge. Existing approaches face a fundament…
Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation law
Jingdong Zhang, Qunxi Zhu, Wei Lin
Accurately finding and predicting dynamics based on the observational data with noise perturbations is of paramount significance but still a major challenge presently. Here, for th…