基于辅助椭圆算子的线性参数抛物型偏微分方程结构可辨识性。

IF 2.2 4区 数学 Q2 BIOLOGY
Yurij Salmaniw, Alexander P Browning
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引用次数: 0

摘要

在解释生物数据时,参数可辨识性通常是数学模型有效应用的必要条件,然而,适用于偏微分方程研究的理论仍然有限。我们提出了一种新的方法来分析结构可辨识性的完全观测抛物线方程,它们的参数是线性的。我们的方法将可辨识性作为一个密切相关的椭圆方程的存在唯一性问题,并对齐次方程,利用著名的Fredholm替代方法来建立无条件可辨识性,以及初始条件和边界条件的特定选择导致不可辨识性的情况。虽然在某种意义上是病态的,但我们证明这种结构可识别性的丧失对实际可识别性有影响;对于空间问题尤其重要,其中初始条件通常受到实验约束的限制。对于具有非线性反应项的情况,辅助椭圆方程解的唯一性对应于可辨识性,在温和的假设下往往导致无条件的全局可辨识性。我们提出了一套简单的标量模型的各种边界条件,包括线性(指数)和非线性(逻辑)源项的分析,以及两种细胞运动模型的特殊情况。最后,我们讨论了这种新的视角如何使发达的分析工具能够推动偏微分方程结构可辨识性理论的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Structural identifiability of linear-in-parameter parabolic PDEs through auxiliary elliptic operators.

Parameter identifiability is often requisite to the effective application of mathematical models in the interpretation of biological data, however theory applicable to the study of partial differential equations remains limited. We present a new approach to structural identifiability analysis of fully observed parabolic equations that are linear in their parameters. Our approach frames identifiability as an existence and uniqueness problem in a closely related elliptic equation and draws, for homogeneous equations, on the well-known Fredholm alternative to establish unconditional identifiability, and cases where specific choices of initial and boundary conditions lead to non-identifiability. While in some sense pathological, we demonstrate that this loss of structural identifiability has ramifications for practical identifiability; important particularly for spatial problems, where the initial condition is often limited by experimental constraints. For cases with nonlinear reaction terms, uniqueness of solutions to the auxiliary elliptic equation corresponds to identifiability, often leading to unconditional global identifiability under mild assumptions. We present analysis for a suite of simple scalar models with various boundary conditions that include linear (exponential) and nonlinear (logistic) source terms, and a special case of a two-species cell motility model. We conclude by discussing how this new perspective enables well-developed analysis tools to advance the developing theory underlying structural identifiability of partial differential equations.

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来源期刊
CiteScore
3.30
自引率
5.30%
发文量
120
审稿时长
6 months
期刊介绍: The Journal of Mathematical Biology focuses on mathematical biology - work that uses mathematical approaches to gain biological understanding or explain biological phenomena. Areas of biology covered include, but are not restricted to, cell biology, physiology, development, neurobiology, genetics and population genetics, population biology, ecology, behavioural biology, evolution, epidemiology, immunology, molecular biology, biofluids, DNA and protein structure and function. All mathematical approaches including computational and visualization approaches are appropriate.
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