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Neural Lyapunov Model Predictive Control
With a growing interest in data-driven control techniques, Model Predictive Control (MPC) provides a significant opportunity to exploit the surplus of data reliably, particularly while taking safety and stability into account.
Real-time Classification from Short Event-Camera Streams using Input-filtering Neural ODEs
INODE is trained like a standard RNN, it learns to discriminate short event sequences and to perform event-by-event online inference.
Neural Lyapunov Model Predictive Control: Learning Safe Global Controllers from Sub-optimal Examples
With a growing interest in data-driven control techniques, Model Predictive Control (MPC) provides an opportunity to exploit the surplus of data reliably, particularly while taking safety and stability into account.
Safe Interactive Model-Based Learning
A min-max control framework, based on alternate minimisation and backpropagation through the forward model, is used for the offline computation of the controller and the safe set.
SNODE: Spectral Discretization of Neural ODEs for System Identification
This paper proposes the use of spectral element methods \citep{canuto_spectral_1988} for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets; \citealp{Chen2018NeuralOD}) for system identification.
Smart energy models for atomistic simulations using a DFT-driven multifidelity approach
Machine-learning techniques such as artificial neural networks are usually employed to work around this limitation and extract the needed parameters from large databases of high-fidelity data, but the latter are often computationally expensive to produce.
