Search Results for author: Alessio Quaglino

Found 6 papers, 0 papers with code

Neural Lyapunov Model Predictive Control

no code implementations28 Sep 2020 Mayank Mittal, Marco Gallieri, Alessio Quaglino, Seyed Sina Mirrazavi Salehian, Jan Koutnik

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.

Continuous Control

Neural Lyapunov Model Predictive Control: Learning Safe Global Controllers from Sub-optimal Examples

no code implementations21 Feb 2020 Mayank Mittal, Marco Gallieri, Alessio Quaglino, Seyed Sina Mirrazavi Salehian, Jan Koutník

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.

Continuous Control

Safe Interactive Model-Based Learning

no code implementations15 Nov 2019 Marco Gallieri, Seyed Sina Mirrazavi Salehian, Nihat Engin Toklu, Alessio Quaglino, Jonathan Masci, Jan Koutník, Faustino Gomez

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.

Safe Exploration

SNODE: Spectral Discretization of Neural ODEs for System Identification

no code implementations ICLR 2020 Alessio Quaglino, Marco Gallieri, Jonathan Masci, Jan Koutník

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

no code implementations21 Aug 2018 Luca Messina, Alessio Quaglino, Alexandra Goryaeva, Mihai-Cosmin Marinica, Christophe Domain, Nicolas Castin, Giovanni Bonny, Rolf Krause

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.

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