Easier access to custom kernels

pnkraemer / probfindiff

Probabilistic numerical finite differences. Compute finite difference weights and differentiation matrices on scattered data sites and with out-of-the-box uncertainty quantification.

https://probfindiff.readthedocs.io

MIT License

10 stars 2 forks source link

Easier access to custom kernels #42

Open pnkraemer opened 2 years ago

pnkraemer commented 2 years ago

Currently, it is possible to pass custom kernels to the functions, but if a user has access to efficient derivatives, that is hard make good use of.