Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations
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Abstract (arXiv)
In chemical reaction network theory, ordinary differential equations are used to model the temporal change of chemical species concentration. As the functional form of these ordinary differential equations systems is derived from an empirical model of the reaction network, it may be incomplete. Our approach aims to elucidate these hidden insights in the reaction network by combining dynamic modelling with deep learning in the form of neural ordinary differential equations. Our contributions not only help to identify the shortcomings of existing empirical models but also assist the design of future reaction networks.
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References · 14
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- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2018Neural Ordinary Differential Equationscited by 3,733
- 2016Density estimation using Real NVPcited by 2,029
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- 2020Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Networkcited by 27
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