Collocation based training of neural ordinary differential equations

DOI10.1515/sagmb-2020-0025OpenAlexW3182302426WikidataQ115235757 ScholiaQ115235757MaRDI QIDQ2236696

Christopher Rackauckas, Michael P. H. Stumpf, Elisabeth Roesch

Publication date: 26 October 2021

Published in: Statistical Applications in Genetics and Molecular Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/sagmb-2020-0025

zbMATH Keywords

dynamical systemscollocationsystems biologyneural ODE

Mathematics Subject Classification ID

Ordinary differential equations (34-XX) Computer science (68-XX)

Related Items (2)

Comparison of neural closure models for discretised PDEs ⋮ Open problems in mathematical biology

Uses Software

• PMTK
• DifferentialEquations.jl
• DiffEqFlux
• Flux
• sugsvarsel
• torchdiffeq
• ANODEs
• NeuralPDE.jl
• GpABC

Cites Work

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• Fast approximate inference for variable selection in Dirichlet process mixtures, with an application to pan-cancer proteomics
• Dynamical systems. Examples of complex behaviour
• Topological sensitivity analysis for systems biology
• Approximation and inference methods for stochastic biochemical kinetics—a tutorial review
• Parameter Estimation for Differential Equation Models Using a Framework of Measurement Error in Regression Models

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