Bayesian uncertainty quantification for data-driven equation learning

Equation learning aims to infer differential equation models from data. While a number of studies have shown that differential equation models can be successfully identified when the data are sufficiently detailed and corrupted with relatively small amounts of noise, the relationship between observa...

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Detalles Bibliográficos
Autores principales: Martina-Perez, Simon, Simpson, Matthew J., Baker, Ruth E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2021
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8548080/
https://www.ncbi.nlm.nih.gov/pubmed/35153587
http://dx.doi.org/10.1098/rspa.2021.0426

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8548080/
https://www.ncbi.nlm.nih.gov/pubmed/35153587
http://dx.doi.org/10.1098/rspa.2021.0426

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