Title data
Grüne, Lars:
Overcoming the curse of dimensionality for approximating Lyapunov functions with deep neural networks under a smallgain condition.
In: IFACPapersOnLine.
(2020)
.
 6 S..
ISSN 24058963
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Project financing: 
Deutsche Forschungsgemeinschaft 

Abstract in another language
We propose a deep neural network architecture for storing approximate Lyapunov functions of systems of ordinary differential equations. Under a smallgain condition on the system, the number of neurons needed for an approximation of a Lyapunov function with fixed accuracy grows only polynomially in the state dimension, i.e., the proposed approach is able to overcome the curse of dimensionality.
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Overcoming the curse of dimensionality for approximating Lyapunov functions with deep neural networks under a smallgain condition. (deposited 27 Jan 2020 14:16)
 Overcoming the curse of dimensionality for approximating Lyapunov functions with deep neural networks under a smallgain condition. (deposited 20 May 2020 07:19) [Currently Displayed]