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Computing Lyapunov functions using deep neural networks

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  • We propose a deep neural network architecture and associated loss functions for a training algorithm for computing approximate Lyapunov functions of systems of nonlinear ordinary differential equations. Under the assumption that the system admits a compositional Lyapunov function, we prove that 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. We show that nonlinear systems satisfying a small-gain condition admit compositional Lyapunov functions. Numerical examples in up to ten space dimensions illustrate the performance of the training scheme.

    Mathematics Subject Classification: Primary:68T07;34D20;Secondary:93D09.


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  • Figure 1.  Neural network with $ 1 $ and $ 2 $ hidden layers

    Figure 2.  Neural network for Lyapunov functions, $ f\in F_1^{d_{\max}} $

    Figure 3.  Neural network for Lyapunov functions, $ f\in F_2^{d_{\max}} $

    Figure 5.  Attempt to compute a Lyapunov function $ W(\cdot;\theta^*) $ (solid) with its orbital derivative $ DW(\cdot;\theta^*)f $ (mesh) for Ex. (12) with loss function (9)

    Figure 4.  Approximate Lyapunov function $ W(\cdot;\theta^*) $ (solid) and its orbital derivative $ DW(\cdot;\theta^*)f $ (mesh) for Example (12) computed with loss function (11)

    Figure 6.  Approximate Lyapunov function $ W(\cdot;\theta^*) $ (solid) and its orbital derivative $ DW(\cdot;\theta^*)f $ (mesh) for Example (13) on $ (x_2,x_8) $-plane

    Figure 7.  Approximate Lyapunov function $ W(\cdot;\theta^*) $ (solid) and its orbital derivative $ DW(\cdot;\theta^*)f $ (mesh) for Example (13) on $ (x_9,x_{10}) $-plane

    Figure 8.  Value of approximate Lyapunov function $ W(x(t);\theta^*) $ along trajectories for initial values $ x_0 = (1,1,1,1,1,1,1,1,1,1)^T $, $ (0,1,0,1,0,1,0,1,0,1)^T $, $ (1,0,0,0,0,0,0,0,0,0)^T $ (left to right)

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