- #1
BWV
- 1,504
- 1,830
As neural networks are 'universal approximators' for nonlinear functions, in general how do they perform in comparison to traditional numerical methods for nonlinear PDEs? Just googling, I can find papers on applications to Navier Stokes and other problems, but I don't really have the background to judge how potentially useful they are. For example, can NNs perform better (i.e. comparable accuracy but less computationally intensive) than current numerical methods for modelling the NS equations?
(this may be better in the Computer Science forum)
https://en.wikipedia.org/wiki/Universal_approximation_theorem
(this may be better in the Computer Science forum)
https://en.wikipedia.org/wiki/Universal_approximation_theorem