Exact hierarchical reductions of dynamical models via linear transformations

Fuente: arXiv
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Autori principali: Demin, Alexander, Demitraki, Elizaveta, Pogudin, Gleb
Natura: Preprint
Pubblicazione: 2023
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author Demin, Alexander
Demitraki, Elizaveta
Pogudin, Gleb
author_facet Demin, Alexander
Demitraki, Elizaveta
Pogudin, Gleb
contents Dynamical models described by ordinary differential equations (ODEs) are a fundamental tool in the sciences and engineering. Exact reduction aims at producing a lower-dimensional model in which each macro-variable can be directly related to the original variables, and it is thus a natural step towards the model's formal analysis and mechanistic understanding. We present an algorithm which, given a polynomial ODE model, computes a longest possible chain of exact linear reductions of the model such that each reduction refines the previous one, thus giving a user control of the level of detail preserved by the reduction. This significantly generalizes over the existing approaches which compute only the reduction of the lowest dimension subject to an approach-specific constraint. The algorithm reduces finding exact linear reductions to a question about representations of finite-dimensional algebras. We provide an implementation of the algorithm, demonstrate its performance on a set of benchmarks, and illustrate the applicability via case studies. Our implementation is freely available at https://github.com/x3042/ExactODEReduction.jl
format Preprint
id arxiv_https___arxiv_org_abs_2301_11653
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Exact hierarchical reductions of dynamical models via linear transformations
Demin, Alexander
Demitraki, Elizaveta
Pogudin, Gleb
Systems and Control
Mathematical Software
Symbolic Computation
Dynamical Systems
34C20, 34-04, 16G10
Dynamical models described by ordinary differential equations (ODEs) are a fundamental tool in the sciences and engineering. Exact reduction aims at producing a lower-dimensional model in which each macro-variable can be directly related to the original variables, and it is thus a natural step towards the model's formal analysis and mechanistic understanding. We present an algorithm which, given a polynomial ODE model, computes a longest possible chain of exact linear reductions of the model such that each reduction refines the previous one, thus giving a user control of the level of detail preserved by the reduction. This significantly generalizes over the existing approaches which compute only the reduction of the lowest dimension subject to an approach-specific constraint. The algorithm reduces finding exact linear reductions to a question about representations of finite-dimensional algebras. We provide an implementation of the algorithm, demonstrate its performance on a set of benchmarks, and illustrate the applicability via case studies. Our implementation is freely available at https://github.com/x3042/ExactODEReduction.jl
title Exact hierarchical reductions of dynamical models via linear transformations
topic Systems and Control
Mathematical Software
Symbolic Computation
Dynamical Systems
34C20, 34-04, 16G10
url https://arxiv.org/abs/2301.11653