Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states
Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …
review the physical principles of BCIs, and underlying novel approaches for registration …
Robustness of LSTM neural networks for multi-step forecasting of chaotic time series
M Sangiorgio, F Dercole - Chaos, Solitons & Fractals, 2020 - Elsevier
Recurrent neurons (and in particular LSTM cells) demonstrated to be efficient when used as
basic blocks to build sequence to sequence architectures, which represent the state-of-the …
basic blocks to build sequence to sequence architectures, which represent the state-of-the …
[HTML][HTML] Reservoir computing as digital twins for nonlinear dynamical systems
We articulate the design imperatives for machine learning based digital twins for nonlinear
dynamical systems, which can be used to monitor the “health” of the system and anticipate …
dynamical systems, which can be used to monitor the “health” of the system and anticipate …
Long-term prediction of chaotic systems with machine learning
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 …
exploited for model-free, data-based prediction of the state evolution of a variety of chaotic …
Machine learning prediction of critical transition and system collapse
To predict a critical transition due to parameter drift without relying on a model is an
outstanding problem in nonlinear dynamics and applied fields. A closely related problem is …
outstanding problem in nonlinear dynamics and applied fields. A closely related problem is …
Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius
A common difficulty in applications of machine learning is the lack of any general principle
for guiding the choices of key parameters of the underlying neural network. Focusing on a …
for guiding the choices of key parameters of the underlying neural network. Focusing on a …
Anticipating synchronization with machine learning
In realistic systems of coupled oscillators, it is desired to predict the onset of synchronization
where the system equations are unknown, raising the need to develop a prediction …
where the system equations are unknown, raising the need to develop a prediction …
Machine learning approach reveals strong link between obliquity amplitude increase and the Mid-Brunhes transition
Abstract The Mid-Brunhes Transition (MBT) refers to the change in the amplitude of glacial-
interglacial cycles around 430 ka BP, with more pronounced, warmer interglacials after ca …
interglacial cycles around 430 ka BP, with more pronounced, warmer interglacials after ca …
Predicting amplitude death with machine learning
In nonlinear dynamics, a parameter drift can lead to a sudden and complete cessation of the
oscillations of the state variables—the phenomenon of amplitude death. The underlying …
oscillations of the state variables—the phenomenon of amplitude death. The underlying …
Reservoir computing on epidemic spreading: A case study on COVID-19 cases
A reservoir computing based echo state network (ESN) is used here for the purpose of
predicting the spread of a disease. The current infection trends of a disease in some …
predicting the spread of a disease. The current infection trends of a disease in some …