Learning partial differential equations for biological transport models from noisy spatio-temporal data

DOI10.1098/rspa.2019.0800zbMath1439.35493arXiv1902.04733OpenAlexW3007112813WikidataQ90576716 ScholiaQ90576716MaRDI QIDQ5114254

G. Michael Lavigne, John Lagergren, Kevin B. Flores, Erica M. Rutter, John T. Nardini

Publication date: 21 June 2020

Published in: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.04733

zbMATH Keywords

parameter estimationnumerical differentiationpartial differential equationssparse regressionequation learningbiological transport

Mathematics Subject Classification ID

Point estimation (62F10) Neural networks for/in biological studies, artificial life and related topics (92B20) PDEs in connection with biology, chemistry and other natural sciences (35Q92)

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Uses Software

• Adam
• foba

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