A Bayesian approach for data-driven dynamic equation discovery

DOI10.1007/s13253-022-00514-1OpenAlexW4292813980WikidataQ113899318 ScholiaQ113899318MaRDI QIDQ2102994

Christopher K. Wikle, Erin M. Schliep, Joshua S. North

Publication date: 12 December 2022

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13253-022-00514-1

zbMATH Keywords

ordinary differential equationnonlinear dynamic equationdynamic discovery uncertainty quantificationprobabilistic system discoverystatistical differential equations

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Related Items (1)

Group projected subspace pursuit for identification of variable coefficient differential equations (GP-IDENT)

Uses Software

• astsa
• Julia
• ForwardDiff
• PySINDy
• PDE-Net

Cites Work

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