Results for Neural Ordinary Differential Equations Inspired Parameterization of Kinetic Models
DOI:10.4121/3662eca5-7077-4ca3-8f66-d051e2c79cbe.v3
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DOI: 10.4121/3662eca5-7077-4ca3-8f66-d051e2c79cbe
DOI: 10.4121/3662eca5-7077-4ca3-8f66-d051e2c79cbe
Datacite citation style
van Lent, Paul; Planken, L.R.(Léon); Bunkova, Olga; Thomas Abeel; Schmitz, Joep (2025): Results for Neural Ordinary Differential Equations Inspired Parameterization of Kinetic Models. Version 3. 4TU.ResearchData. dataset. https://doi.org/10.4121/3662eca5-7077-4ca3-8f66-d051e2c79cbe.v3
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
Results from the computational experiments on simulated and experimental time-series data as described in the paper Neural Ordinary Differential Equations Inspired
Parameterization of Kinetic Models. Details on the experiments can be found in the readme of https://github.com/AbeelLab/jaxkineticmodel
History
- 2024-12-20 first online
- 2025-05-20 published, posted
Publisher
4TU.ResearchDataFormat
csvFunding
- AI4b.io [more info...]
Organizations
AI4b.ioTU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, The Delft Bioinformatics Lab
DSM-Firmenich
DATA
Files (1)
- 2,734,027,867 bytesMD5:
df4040293be14ae0e8d061b6f27ce651
jax_neural_odes.zip