Search Results for author:
Found 17 papers, 3 papers with code
Model-free tracking control of complex dynamical trajectories with machine learning
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.
Machine-learning parameter tracking with partial state observation
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.
Random forests for detecting weak signals and extracting physical information: a case study of magnetic navigation
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.
Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius
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.
Long-term prediction of chaotic systems with recurrent neural networks
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.
Machine learning prediction of critical transition and system collapse
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.
Adaptable Hamiltonian neural networks
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.
Tomography of time-dependent quantum spin networks with machine learning
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.
Anticipating synchronization with machine learning
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.
Synchronization within synchronization: transients and intermittency in ecological networks
Transients are fundamental to ecological systems with significant implications to management, conservation, and biological control.
Predicting extreme events from data using deep machine learning: when and where
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.
Digital twins of nonlinear dynamical systems
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.
Emergence of a stochastic resonance in machine learning
Can noise be beneficial to machine-learning prediction of chaotic systems?
Generating extreme quantum scattering in graphene with machine learning
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.
Digital twins of nonlinear dynamical systems: A perspective
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.
Rate-induced tipping in complex high-dimensional ecological networks
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.
Machine-learning prediction of tipping and collapse of the Atlantic Meridional Overturning Circulation
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.
