Bayesian uncertainty quantification for data-driven equation learning

Simon Martina-Perez, Matthew J. Simpson, Ruth E. Baker

Open source

DOI: 10.1098/rspa.2021.0426
Published: 2021-10
Container: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
Publisher: The Royal Society
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1098/rspa.2021.0426,
  title = {Bayesian uncertainty quantification for data-driven equation learning},
  author = {Simon Martina-Perez and Matthew J. Simpson and Ruth E. Baker},
  year = {2021},
  journal = {Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  doi = {10.1098/rspa.2021.0426},
  url = {https://doi.org/10.1098/rspa.2021.0426}
}

RIS

TY  - JOUR
TI  - Bayesian uncertainty quantification for data-driven equation learning
AU  - Simon Martina-Perez
AU  - Matthew J. Simpson
AU  - Ruth E. Baker
PY  - 2021
JO  - Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences
DO  - 10.1098/rspa.2021.0426
UR  - https://doi.org/10.1098/rspa.2021.0426
ER  -

APA

Martina-Perez, S., Simpson, M. J., & Baker, R. E. (2021). Bayesian uncertainty quantification for data-driven equation learning. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. https://doi.org/10.1098/rspa.2021.0426

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crossref · retrieved 2026-09-25T11:41:27.945Z