Making the package more maintainable

[avik-pal]

I did an initial round of cleanup in #882, but there's a lot of unwanted code that should be purged, and most of the handling should be forwarded to Lux.

• GPU Support: Currently there is an adapt to copy over anything that is not on GPU to GPU on every call to the function. IMO this should be completely removed, and if user calls a model which is partially on GPU then it should be an error (similar to Lux)
  • We can preserve the current behavior for scalars
• Phi / ODEPhi need to be rewritten as a Lux layer, that un-blocks all current shortcomings with nested AD
• Annotate the closures with @closure to avoid boxing
• Remove the logging subpackage with a extension refactor: remove NeuralPDELogging in-favor of extension #901
• Move Bayesian ones into an extension (or a subpackage BayesianNeuralPDE)? I am pretty sure the number of users for those is quite small but those packages add a significant load time
• reproducibility -- all models should take in an rng instead of relying on the global RNG

P.S. Just because I am opening this issue doesn't mean I am taking it upon me to do this 😓

[ChrisRackauckas]

Agreed with all of these.

[sathvikbhagavan]

I can help out a bit.

For 1 - GPU Support for NNODE, users should provide the model, initial conditions and parameters in gpu arrays in order to not error? Also, initially I thought GPU works with NNODE now, I wanted to confirm it - I was working on a #866 which implemented a custom broadcast, is it still needed?

For 2 - Will that fix using autodiff with NNODE?

[avik-pal]

1. Correct. Does GPU already not work with NNODE? I thought I fixed it
2. Yes that's the main goal

[ParamThakkar123]

BNNODE, ahmc_bayesian_pinn_ode, ahmc_bayesian_pinn_pde, BPINNsolution, BayesianPINN, these are the bayesian ones right ??

[ChrisRackauckas]

yes

[ParamThakkar123]

I can help with this. Shifting would be relatively very fast and easy

[ParamThakkar123]

Meanwhile I can also try to analyse and fix other requirements