Inverse Diffusivity
Recovers a space-dependent diffusivity D(x) represented as a neural network Field, rather than a
single scalar parameter.
Problem
\[
\frac{\partial u}{\partial t} = \nabla \cdot \bigl(D(x)\,\nabla u\bigr)
\]
where D(x) is unknown and learned as a neural network.
Features Demonstrated
- Function-valued parameter recovery (D(x) as a
Field) - Composable differential operators from
anypinn.lib.diff - PDE inverse problem with spatially varying coefficients
Results
