Learning the intrinsic dimensionality of Fermi-Pasta-Ulam-Tsingou trajectories: A nonlinear approach using a deep autoencoder model.

Marchetti G

DOI: 10.1063/5.0335458
Published: 2026 Sep 1
Container: Chaos (Woodbury, N.Y.)
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1063/5.0335458,
  title = {Learning the intrinsic dimensionality of Fermi-Pasta-Ulam-Tsingou trajectories: A nonlinear approach using a deep autoencoder model.},
  author = {Marchetti G},
  year = {2026},
  journal = {Chaos (Woodbury, N.Y.)},
  doi = {10.1063/5.0335458},
  url = {https://doi.org/10.1063/5.0335458}
}

RIS

TY  - JOUR
TI  - Learning the intrinsic dimensionality of Fermi-Pasta-Ulam-Tsingou trajectories: A nonlinear approach using a deep autoencoder model.
AU  - Marchetti G
PY  - 2026
JO  - Chaos (Woodbury, N.Y.)
DO  - 10.1063/5.0335458
UR  - https://doi.org/10.1063/5.0335458
ER  -

APA

G, M. (2026). Learning the intrinsic dimensionality of Fermi-Pasta-Ulam-Tsingou trajectories: A nonlinear approach using a deep autoencoder model.. Chaos (Woodbury, N.Y.). https://doi.org/10.1063/5.0335458

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pubmed · retrieved 2026-09-25T09:18:30.307Z