3 papers
math.NA2024
Towards optimal hierarchical training of neural networks
Michael Feischl, Alexander Rieder, Fabian Zehetgruber
We propose a hierarchical training algorithm for standard feed-forward neural networks that adaptively extends the network architecture as soon as the optimization reaches a statio…
math.NA2024
A -version of convolution quadrature in wave propagation
Alexander Rieder
We consider a novel way of discretizing wave scattering problems using the general formalism of convolution quadrature, but instead of reducing the timestep size (-method), we a…
math.NA2024
FEM-BEM coupling for the high-frequency Helmholtz problem
Jens Markus Melenk, Ilaria Perugia, Alexander Rieder
We present a wavenumber-explicit analysis of FEM-BEM coupling methods for time-harmonic Helmholtz problems proposed in arXiv:2004.03523 for conforming discretizations and in arXiv:…