Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Quaglino, Alessio"'
Autor:
Bader, Seif Ben, Harbrecht, Helmut, Krause, Rolf, Multerer, Michael, Quaglino, Alessio, Schmidlin, Marc
We present a novel approach which aims at high-performance uncertainty quantification for cardiac electrophysiology simulations. Employing the monodomain equation to model the transmembrane potential inside the cardiac cells, we evaluate the effect o
Externí odkaz:
http://arxiv.org/abs/2105.02007
Autor:
Giannone, Giorgio, Anoosheh, Asha, Quaglino, Alessio, D'Oro, Pierluca, Gallieri, Marco, Masci, Jonathan
Event-based cameras are novel, efficient sensors inspired by the human vision system, generating an asynchronous, pixel-wise stream of data. Learning from such data is generally performed through heavy preprocessing and event integration into images.
Externí odkaz:
http://arxiv.org/abs/2004.03156
Neural Lyapunov Model Predictive Control: Learning Safe Global Controllers from Sub-optimal Examples
Autor:
Mittal, Mayank, Gallieri, Marco, Quaglino, Alessio, Salehian, Seyed Sina Mirrazavi, Koutník, Jan
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. In many real-world and industrial
Externí odkaz:
http://arxiv.org/abs/2002.10451
Autor:
Gallieri, Marco, Salehian, Seyed Sina Mirrazavi, Toklu, Nihat Engin, Quaglino, Alessio, Masci, Jonathan, Koutník, Jan, Gomez, Faustino
Control applications present hard operational constraints. A violation of these can result in unsafe behavior. This paper introduces Safe Interactive Model Based Learning (SiMBL), a framework to refine an existing controller and a system model while
Externí odkaz:
http://arxiv.org/abs/1911.06556
We present a novel approach aimed at high-performance uncertainty quantification for time-dependent problems governed by partial differential equations. In particular, we consider input uncertainties described by a Karhunen-Loeeve expansion and compu
Externí odkaz:
http://arxiv.org/abs/1911.06066
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. This is achieved by expres
Externí odkaz:
http://arxiv.org/abs/1906.07038
Autor:
Messina, Luca, Quaglino, Alessio, Goryaeva, Alexandra, Marinica, Mihai-Cosmin, Domain, Christophe, Castin, Nicolas, Bonny, Giovanni, Krause, Rolf
Publikováno v:
Nucl. Instrum. Methods Phys. Res. B 483, 15-21 (2020)
The reliability of atomistic simulations depends on the quality of the underlying energy models providing the source of physical information, for instance for the calculation of migration barriers in atomistic Kinetic Monte Carlo simulations. Accurat
Externí odkaz:
http://arxiv.org/abs/1808.06935
Multifidelity Monte Carlo methods rely on a hierarchy of possibly less accurate but statistically correlated simplified or reduced models, in order to accelerate the estimation of statistics of high-fidelity models without compromising the accuracy o
Externí odkaz:
http://arxiv.org/abs/1807.10521
Autor:
Quaglino, Alessio, Krause, Rolf
We present a first step towards a multigrid method for solving the min-cost flow problem. Specifically, we present a strategy that takes advantage of existing black-box fast iterative linear solvers, i.e. algebraic multigrid methods. We show with sta
Externí odkaz:
http://arxiv.org/abs/1612.00201
Multiscale models allow for the treatment of complex phenomena involving different scales, such as remodeling and growth of tissues, muscular activation, and cardiac electrophysiology. Numerous numerical approaches have been developed to simulate mul
Externí odkaz:
http://arxiv.org/abs/1609.07719