I want to add another twist to the parametrized solution shown in “Parameterized Simulations and Design Optimization: 3D heat sink”
What if you have not just 6 dimensions that are a possible parameter, but a whole function?
Simple example: f(x)_xx + k(x)*f(x) = 0,
but there are many possible functions k(x) and you want the network to learn the output f(x) under many k(x), so in the future you have a new k(x) as an input and want the network to get the most likely answer f(x).
Any ideas? Thx in advance.