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pro vyhledávání: '"Martina Perez, Simon"'
Epithelial monolayers are some of the best-studied models for collective cell migration due to their abundance in multicellular systems and their tractability. Experimentally, the collective migration of epithelial monolayers can be robustly steered
Externí odkaz:
http://arxiv.org/abs/2402.08700
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
Externí odkaz:
http://arxiv.org/abs/2102.11629
Autor:
Martina Perez, Simon1 (AUTHOR) martinaperez@maths.ox.ac.uk, Sailem, Heba2 (AUTHOR), Baker, Ruth E.1 (AUTHOR)
Publikováno v:
PLoS Computational Biology. 6/21/2022, Vol. 18 Issue 6, p1-25. 25p. 2 Black and White Photographs, 1 Diagram, 8 Graphs.
Supplementary Information from 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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f1506884470efb93e6a6ff07d47074b
Autor:
Martina-Perez SF; Mathematical Institute, University of Oxford, Oxford, UK. martinaperez@maths.ox.ac.uk., Breinyn IB; Department of Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA., Cohen DJ; Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA., Baker RE; Mathematical Institute, University of Oxford, Oxford, UK.
Publikováno v:
Bulletin of mathematical biology [Bull Math Biol] 2024 Jun 19; Vol. 86 (8), pp. 95. Date of Electronic Publication: 2024 Jun 19.
Autor:
Martina-Perez SF; Mathematical Institute, University of Oxd, Oxford, United Kingdom., Breinyn IB; Department of Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA., Cohen DJ; Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA., Baker RE; Mathematical Institute, University of Oxd, Oxford, United Kingdom.
Publikováno v:
BioRxiv : the preprint server for biology [bioRxiv] 2024 Feb 29. Date of Electronic Publication: 2024 Feb 29.
Autor:
Martina-Perez S; Mathematical Institute, University of Oxford, Oxford, UK., Simpson MJ; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia., Baker RE; Mathematical Institute, University of Oxford, Oxford, UK.
Publikováno v:
Proceedings. Mathematical, physical, and engineering sciences [Proc Math Phys Eng Sci] 2021 Oct; Vol. 477 (2254), pp. 20210426. Date of Electronic Publication: 2021 Oct 27.