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fixed some missing $ in physicalloss.md

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Albert Alonso 2021-09-12 18:35:49 +02:00
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physicalloss.md
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@ -29,7 +29,7 @@ $$
$$
where the $_{\mathbf{x}}$ subscripts denote spatial derivatives with respect to one of the spatial dimensions
of higher and higher order (this can of course also include mixed derivatives with respect to different axes). \mathbf{u}_t denotes the changes over time.
of higher and higher order (this can of course also include mixed derivatives with respect to different axes). $\mathbf{u}_t$ denotes the changes over time.
In this context, we can approximate the unknown $\mathbf{u}$ itself with a neural network. If the approximation, which we call $\tilde{\mathbf{u}}$, is accurate, the PDE should be satisfied naturally. In other words, the residual R should be equal to zero: