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Found 8 papers, 3 papers with code
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.
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.
Underwater Acoustic Communication Channel Modeling using Reservoir Computing
Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment.
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.
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.
Deep Multimodal Transfer-Learned Regression in Data-Poor Domains
In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc.
