Search Results for author:
Found 4 papers, 3 papers with code
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.
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.
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.
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.
