Conservative deep neural networks for modeling competition of ribosomes with extended length

DOI10.1016/j.physd.2024.134415MaRDI QIDQ6650131

Naman Krishna Pande, Aditi Jain, Arun N. Kumar, Arvind Kumar Gupta

Publication date: 6 December 2024

Published in: Physica D (Search for Journal in Brave)

zbMATH Keywords

ordinary differential equationsfirst integralcontraction theorydeep neural networksribosome flow model with extended objects

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Biochemistry, molecular biology (92C40)

Cites Work

• Contraction after small transients
• On contraction analysis for non-linear systems
• Multilayer feedforward networks are universal approximators
• An efficient Runge-Kutta \((4,5)\) pair
• A fixed point theorem for bounded dynamical systems
• Bridging the gap: machine learning to resolve improperly modeled dynamics
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Physics-informed neural network methods based on Miura transformations and discovery of new localized wave solutions
• Competitive and Cooperative Tridiagonal Systems of Differential Equations
• Asymmetric simple exclusion model with local inhomogeneity

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