Knowledge-informed neuro-integrators for aggregation kinetics

DOI10.1016/j.cnsns.2024.107870arXiv2312.06857OpenAlexW4391365406MaRDI QIDQ6121869

Nikolai V. Brilliantov, I. Yu. Tyukin, D. Lukashevich

Publication date: 27 February 2024

Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2312.06857

zbMATH Keywords

neural networksmodel reductionaggregation kineticsmachine leaning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Kinetics in biochemical problems (pharmacokinetics, enzyme kinetics, etc.) (92C45) Numerical methods for initial value problems involving ordinary differential equations (65L05) Numerical solution of boundary value problems involving ordinary differential equations (65L10) Statistical aspects of big data and data science (62R07)

Cites Work

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• A fast numerical method for the Cauchy problem for the Smoluchowski equation
• Invariant manifolds for physical and chemical kinetics
• Low-rank method for fast solution of generalized Smoluchowski equations
• Adaptivity with moving grids
• On parameter and structural identifiability: Nonunique observability/reconstructibility for identifiable systems, other ambiguities, and new definitions
• Parameter Estimation of Sigmoid Superpositions: Dynamical System Approach
• Deep Learning: An Introduction for Applied Mathematicians
• Neural network approximation
• Approximation by superpositions of a sigmoidal function

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