1 code implementation • 21 Feb 2024 • Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen, Ying-Cheng Lai
It was recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth's anomaly magnetic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment.
no code implementations • 21 Feb 2024 • Shirin Panahi, Ling-Wei Kong, Mohammadamin Moradi, Zheng-Meng Zhai, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
Recent research on the Atlantic Meridional Overturning Circulation (AMOC) raised concern about its potential collapse through a tipping point due to the climate-change caused increase in the freshwater input into the North Atlantic.
1 code implementation • 15 Nov 2023 • Zheng-Meng Zhai, Mohammadamin Moradi, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
In particular, with training data from a subset of the dynamical variables of the system for a small number of known parameter values, the framework is able to accurately predict the parameter variations in time.
no code implementations • 15 Nov 2023 • Shirin Panahi, Younghae Do, Alan Hastings, Ying-Cheng Lai
In an ecosystem, environmental changes as a result of natural and human processes can cause some key parameters of the system to change with time.
1 code implementation • Nature Communications 2023 • Zheng-Meng Zhai, Mohammadamin Moradi, Ling-Wei Kong, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
We develop a model-free, machine-learning framework to control a two-arm robotic manipulator using only partially observed states, where the controller is realized by reservoir computing.
no code implementations • 20 Sep 2023 • Ying-Cheng Lai
In particular, if the digital twin forecasts a possible system collapse in the future due to parameter drifting as caused by environmental changes or perturbations, an optimal control strategy can be devised and executed as early intervention to prevent the collapse.
no code implementations • 13 Dec 2022 • Chen-Di Han, Ying-Cheng Lai
There are applications such as cloaking or superscattering where the challenging problem of inverse design needs to be solved: finding a quantum-dot structure according to certain desired functional characteristics.
no code implementations • 15 Nov 2022 • Zheng-Meng Zhai, Ling-Wei Kong, Ying-Cheng Lai
Can noise be beneficial to machine-learning prediction of chaotic systems?
no code implementations • 5 Oct 2022 • Ling-Wei Kong, Yang Weng, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
We articulate the design imperatives for machine-learning based digital twins for nonlinear dynamical systems subject to external driving, which can be used to monitor the ``health'' of the target system and anticipate its future collapse.
no code implementations • 31 Mar 2022 • Junjie Jiang, Zi-Gang Huang, Celso Grebogi, Ying-Cheng Lai
We develop a deep convolutional neural network (DCNN) based framework for model-free prediction of the occurrence of extreme events both in time ("when") and in space ("where") in nonlinear physical systems of spatial dimension two.
no code implementations • 15 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.
no code implementations • 13 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.
no code implementations • 25 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.
no code implementations • 2 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.
no code implementations • 20 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.
no code implementations • 6 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.
no code implementations • 10 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.