Efficient and scalable prediction of stochastic reaction-diffusion processes using graph neural networks
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Publication:6620156
DOI10.1016/j.mbs.2024.109248MaRDI QIDQ6620156
Ramon Grima, Zhixing Cao, Xiao-Ming Fu, Rui Chen, Libin Xu, Wei-Min Zhong, Xinyi Zhou
Publication date: 16 October 2024
Published in: Mathematical Biosciences (Search for Journal in Brave)
Artificial neural networks and deep learning (68T07) Reaction-diffusion equations (35K57) Biochemistry, molecular biology (92C40) Cell biology (92C37)
Cites Work
- Multilayer feedforward networks are universal approximators
- Deep abstractions of chemical reaction networks
- Automated deep abstractions for stochastic chemical reaction networks
- Spatial stochastic intracellular kinetics: a review of modelling approaches
- A probabilistic framework for particle-based reaction-diffusion dynamics using classical Fock space representations
- Reaction-Diffusion Model as a Framework for Understanding Biological Pattern Formation
- Stochastic Modelling of Reaction–Diffusion Processes
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