Data field theory: a geometric framework for learning on Riemannian manifolds with synthetic validation and limitation analysis.

Nehzati M

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DOI: 10.3389/fdata.2026.1752468
Published: 2026
Container: Frontiers in big data
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/fdata.2026.1752468,
  title = {Data field theory: a geometric framework for learning on Riemannian manifolds with synthetic validation and limitation analysis.},
  author = {Nehzati M},
  year = {2026},
  journal = {Frontiers in big data},
  doi = {10.3389/fdata.2026.1752468},
  url = {https://doi.org/10.3389/fdata.2026.1752468}
}

RIS

TY  - JOUR
TI  - Data field theory: a geometric framework for learning on Riemannian manifolds with synthetic validation and limitation analysis.
AU  - Nehzati M
PY  - 2026
JO  - Frontiers in big data
DO  - 10.3389/fdata.2026.1752468
UR  - https://doi.org/10.3389/fdata.2026.1752468
ER  -

APA

M, N. (2026). Data field theory: a geometric framework for learning on Riemannian manifolds with synthetic validation and limitation analysis.. Frontiers in big data. https://doi.org/10.3389/fdata.2026.1752468

Source records

pubmed · retrieved 2026-11-10T18:10:06.372Z
europe-pmc · retrieved 2026-11-10T18:10:06.349Z
doaj · retrieved 2026-11-10T18:10:06.333Z