Algebraic network reconstruction of discrete dynamical systems

IF 1 3区 数学 Q3 MATHEMATICS, APPLIED Advances in Applied Mathematics Pub Date : 2024-09-04 DOI:10.1016/j.aam.2024.102760
Heather A. Harrington , Mike Stillman , Alan Veliz-Cuba
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引用次数: 0

Abstract

We present a computational algebra solution to reverse engineering the network structure of discrete dynamical systems from data. We use pseudomonomial ideals to determine dependencies between variables that encode constraints on the possible wiring diagrams underlying the process generating the discrete-time, continuous-space data. Our work assumes that each variable is either monotone increasing or decreasing. We prove that with enough data, even in the presence of small noise, our method can reconstruct the correct unique wiring diagram.

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离散动力系统的代数网络重构
我们提出了一种计算代数解决方案,用于从数据中逆向工程离散动力系统的网络结构。我们使用伪自治理想来确定变量之间的依赖关系,这些变量编码了对产生离散时间、连续空间数据的过程的可能线路图的约束。我们的工作假设每个变量都是单调递增或递减的。我们证明,只要有足够多的数据,即使存在较小的噪声,我们的方法也能重建正确的唯一布线图。
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来源期刊
Advances in Applied Mathematics
Advances in Applied Mathematics 数学-应用数学
CiteScore
2.00
自引率
9.10%
发文量
88
审稿时长
85 days
期刊介绍: Interdisciplinary in its coverage, Advances in Applied Mathematics is dedicated to the publication of original and survey articles on rigorous methods and results in applied mathematics. The journal features articles on discrete mathematics, discrete probability theory, theoretical statistics, mathematical biology and bioinformatics, applied commutative algebra and algebraic geometry, convexity theory, experimental mathematics, theoretical computer science, and other areas. Emphasizing papers that represent a substantial mathematical advance in their field, the journal is an excellent source of current information for mathematicians, computer scientists, applied mathematicians, physicists, statisticians, and biologists. Over the past ten years, Advances in Applied Mathematics has published research papers written by many of the foremost mathematicians of our time.
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