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Low-rank kernel approximation of Lyapunov functions using neural networks

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  • We study the use of single hidden layer neural networks for the approximation of Lyapunov functions in autonomous ordinary differential equations. In particular, we focus on the connection between this approach and that of the meshless collocation method using reproducing kernel Hilbert spaces. It is shown that under certain conditions, an optimised neural network is functionally equivalent to the RKHS generalised interpolant solution corresponding to a kernel function that is implicitly defined by the neural network. We demonstrate convergence of the neural network approximation using several numerical examples, and compare with approximations obtained by the meshless collocation method. Finally, motivated by our theoretical and numerical findings, we propose a new iterative algorithm for the approximation of Lyapunov functions using single hidden layer neural networks.

    Mathematics Subject Classification: Primary: 37B25, 68T07; Secondary: 46E22.

    Citation:

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  • Figure 1.  Left: convergence of Lyapunov function approximation for generalised interpolant (kernel) and neural network models. The neural network converges more quickly with respect to $ n $. Right: the RKHS norm of the generalised interpolant grows as the dataset size $ n $ increases and the approximation improves, whereas the Barron norm of the neural network is bounded

    Figure 2.  Contour plots showing the approximations of the Lyapunov function $ V $ for the time-reversed van der Pol equations (29). On the left is the kernel approximation using the Gaussian RKHS $ \mathcal{H}_{K_G} $. In the rightmost four plots are neural network approximations with fixed input-hidden layer weights, for increasing values of hidden width $ m $. The approximation that achieves the lowest test error is the neural network with $ m = 200 $

    Figure 3.  a) Training and test loss for the neural network approximations for increasing values of $ m $. Small values of $ m $ exhibit underfitting, whilst large values of $ m $ (as well as the Gaussian kernel generalised interpolant) overfit. The best approximation was given by the neural network with $ m = 200 $. b) Training time for the networks increases significantly with $ m $. This can be rectified by scaling the learning rate appropriately (see text for further details)

    Figure 4.  Contour plots showing the Lyapunov function approximations for the neural network (21) for varying hidden widths $ m $, where the parameters $ ({\bf{w}}_k, b_k)_{k = 1}^m $ are not trained. Top row: the network function approximations for a fixed dataset size $ n = 90 $ as in Section 4.2. Bottom row: network function approximations from training on randomly sampled minibatches

    Figure 5.  Contour plots showing the kernels $ K_{NN} $ learned by the neural networks for varying widths $ m $. As $ m $ increases, $ K_{NN} $ converges to a fixed, translation-invariant kernel function

    Figure 6.  Test loss vs network width $ m $ for the networks obtained by Algorithm 1 for system (30), using the neural network model (21)

    Figure 7.  Level sets of the Lyapunov function approximation for system (30), given by the neural network model (21) with $ m = 102400 $

    Table 1.  Test error for network models of varying widths $ m $, with a fixed dataset size as in Section 4.2, and an effectively infinite dataset size created through randomly sampled minibatches

    $ m $ 40 80 200 400 800 1600 3200
    Fixed dataset 2.229 0.959 0.772 0.855 0.684 0.949 1.612
    Random minibatches 0.851 0.395 0.312 0.238 0.207 0.178 0.120
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    Table 2.  Test loss for network models of varying widths $ m $, where the first layer weights $ ({\bf{w}}_k, b_k)_{k = 1}^m $ are untrained (fixed kernel $ K_{NN} $) and trained (variable kernel $ K_{NN} $)

    $ m $ 40 80 200 400 800 1600 3200
    Fixed $ K_{NN} $ 0.851 0.417 0.312 0.238 0.207 0.178 0.120
    Variable $ K_{NN} $ 0.453 0.270 0.189 0.103 0.073 0.076 0.063
     | Show Table
    DownLoad: CSV
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