Designing rotationally invariant neural networks from PDEs and variational methods

Tobias Alt, Karl Schrader, Joachim Weickert, Pascal Peter, Matthias Augustin

Open source

DOI: 10.1007/s40687-022-00339-x
Published: 2022-08-04
Container: Research in the Mathematical Sciences
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1007/s40687-022-00339-x,
  title = {Designing rotationally invariant neural networks from PDEs and variational methods},
  author = {Tobias Alt and Karl Schrader and Joachim Weickert and Pascal Peter and Matthias Augustin},
  year = {2022},
  journal = {Research in the Mathematical Sciences},
  doi = {10.1007/s40687-022-00339-x},
  url = {https://doi.org/10.1007/s40687-022-00339-x}
}

RIS

TY  - JOUR
TI  - Designing rotationally invariant neural networks from PDEs and variational methods
AU  - Tobias Alt
AU  - Karl Schrader
AU  - Joachim Weickert
AU  - Pascal Peter
AU  - Matthias Augustin
PY  - 2022
JO  - Research in the Mathematical Sciences
DO  - 10.1007/s40687-022-00339-x
UR  - https://doi.org/10.1007/s40687-022-00339-x
ER  -

APA

Alt, T., Schrader, K., Weickert, J., Peter, P., & Augustin, M. (2022). Designing rotationally invariant neural networks from PDEs and variational methods. Research in the Mathematical Sciences. https://doi.org/10.1007/s40687-022-00339-x

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crossref · retrieved 2026-11-10T12:43:12.347Z