2 papers
cs.LG2026
Derivative Computation in PINNs: Automatic Differentiation, Finite Differences and Beyond
Maciej J. Mikulski, Tadeusz Uhl
We systematically investigate finite-difference (FD) derivative computation in Physics-Informed Neural Networks (PINNs) as an alternative to automatic differentiation (AD). On thre…
stat.ML2019
Toroidal AutoEncoder
Maciej Mikulski, Jaroslaw Duda
Enforcing distributions of latent variables in neural networks is an active subject. It is vital in all kinds of generative models, where we want to be able to interpolate between…