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Jun 18, 2025
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Update lotka.volterra.rst #1999

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2 changes: 1 addition & 1 deletion docs/demos/pinn_forward/lotka.volterra.rst
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Expand Up @@ -82,7 +82,7 @@ We have 3000 training residual points inside the domain and 2 points on the boun
initializer = "Glorot normal"
net = dde.nn.FNN(layer_size, activation, initializer)

This is a neural network of depth 7 with 6 hidden layers of width 50. We use :math:`\tanh` as the activation function. Since we expect to have periodic behavior in the Lotka-Volterra equation, we add a feature layer with :math:`\sin(kt)`. This forces the prediction to be periodic and therefore more accurate.
This is a neural network of depth 7 with 6 hidden layers of width 64. We use :math:`\tanh` as the activation function. Since we expect to have periodic behavior in the Lotka-Volterra equation, we add a feature layer with :math:`\sin(kt)`. This forces the prediction to be periodic and therefore more accurate.

.. code-block:: python

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