Search Results for author: Ying-Cheng Lai

Found 7 papers, 0 papers with code

Tomography of time-dependent quantum spin networks with machine learning

no code implementations15 Mar 2021 Chen-Di Han, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai

In particular, we develop a deep learning algorithm according to some physics motivated loss function based on the Heisenberg equation, which "forces" the neural network to follow the quantum evolution of the spin variables.

Time Series

Anticipating synchronization with machine learning

no code implementations13 Mar 2021 Huawei Fan, Ling-Wei Kong, Ying-Cheng Lai, Xingang Wang

In applications of dynamical systems, situations can arise where it is desired to predict the onset of synchronization as it can lead to characteristic and significant changes in the system performance and behaviors, for better or worse.

Time Series

Adaptable Hamiltonian neural networks

no code implementations25 Feb 2021 Chen-Di Han, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai

The rapid growth of research in exploiting machine learning to predict chaotic systems has revived a recent interest in Hamiltonian Neural Networks (HNNs) with physical constraints defined by the Hamilton's equations of motion, which represent a major class of physics-enhanced neural networks.

Time Series

Machine learning prediction of critical transition and system collapse

no code implementations2 Dec 2020 Ling-Wei Kong, Hua-Wei Fan, Celso Grebogi, Ying-Cheng Lai

Remarkably, for a parameter drift through the critical point, the machine with the input parameter channel is able to predict not only that the system will be in a transient state, but also the average transient time before the final collapse.

Synchronization within synchronization: transients and intermittency in ecological networks

no code implementations20 Nov 2020 Huawei Fan, Ling-Wei Kong, Xingang Wang, Alan Hastings, Ying-Cheng Lai

Transients are fundamental to ecological systems with significant implications to management, conservation, and biological control.

Long-term prediction of chaotic systems with recurrent neural networks

no code implementations6 Mar 2020 Huawei Fan, Junjie Jiang, Chun Zhang, Xingang Wang, Ying-Cheng Lai

Reservoir computing systems, a class of recurrent neural networks, have recently been exploited for model-free, data-based prediction of the state evolution of a variety of chaotic dynamical systems.

Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius

no code implementations10 Oct 2019 Junjie Jiang, Ying-Cheng Lai

Focusing on a class of recurrent neural networks - reservoir computing systems that have recently been exploited for model-free prediction of nonlinear dynamical systems, we uncover a surprising phenomenon: the emergence of an interval in the spectral radius of the neural network in which the prediction error is minimized.

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