End-to-end robust system discovery in electrical dynamical systems using scientific machine learning

Jhelum Chakravorty, Nicolò Ripamonti, Tor Laneryd

Open source

DOI: 10.1038/s44172-026-00689-2
Published: 2026-06-16
Container: Communications Engineering
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1038/s44172-026-00689-2,
  title = {End-to-end robust system discovery in electrical dynamical systems using scientific machine learning},
  author = {Jhelum Chakravorty and Nicolò Ripamonti and Tor Laneryd},
  year = {2026},
  journal = {Communications Engineering},
  doi = {10.1038/s44172-026-00689-2},
  url = {https://doi.org/10.1038/s44172-026-00689-2}
}

RIS

TY  - JOUR
TI  - End-to-end robust system discovery in electrical dynamical systems using scientific machine learning
AU  - Jhelum Chakravorty
AU  - Nicolò Ripamonti
AU  - Tor Laneryd
PY  - 2026
JO  - Communications Engineering
DO  - 10.1038/s44172-026-00689-2
UR  - https://doi.org/10.1038/s44172-026-00689-2
ER  -

APA

Chakravorty, J., Ripamonti, N., & Laneryd, T. (2026). End-to-end robust system discovery in electrical dynamical systems using scientific machine learning. Communications Engineering. https://doi.org/10.1038/s44172-026-00689-2

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crossref · retrieved 2026-09-26T13:51:26.919Z