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arXiv 2312.09653math.APq-bio.PE
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关于捕食者-猎物模型中的反问题

On inverse problems in predator-prey models

Yuhan Li, Hongyu Liu, Catharine W. K. Lo

AI总结:

本文研究了 Lotka-Volterra 捕食者-猎物模型中确定相互作用项系数的反问题,通过改进高阶变分方法确保解的非负性,实现了对所有相互作用项的唯一可识别性恢复,并验证了其在三种具体模型上的应用。

摘要:

本文考虑了在某些 Lotka-Volterra 模型中确定相互作用项系数的反问题,并得到了其非负解的边界观测的支持。在物理背景中,捕食者-猎物模型的解代表捕食者和猎物的种群密度且是非负的,这是我们反问题研究中的一个关键挑战。我们主要关注唯一可识别性问题,并使用高阶变分方法来解决它,这是由第二作者及其合作者引入的一种相对较新的技术。该方法能确保解的正性,并在其他具有非负性要求的物理模型中具有更广泛的适用性。我们的研究通过选择一个更一般的解 $(u_0,v_0)$ 进行展开来改进此方法,实现了对所有相互作用项的恢复。借此,我们改进了先前的结果,并将其应用于物理模型,以恢复系统中关于压缩、猎物攻击、拥挤、承载能力及许多其他相互作用因素的系数。最后,我们应用我们的结果研究了三个具体案例:hydra-effects 模型、Holling-Tanner 模型和经典的 Lotka-Volterra 模型。

英文摘要:

In this paper, we consider the inverse problem of determining the coefficients of interaction terms within some Lotka-Volterra models, with support from boundary observation of its non-negative solutions. In the physical background, the solutions to the predator-prey model stand for the population densities for predator and prey and are non-negative, which is a critical challenge in our inverse problem study. We mainly focus on the unique identifiability issue and tackle it with the high-order variation method, a relatively new technique introduced by the second author and his collaborators. This method can ensure the positivity of solutions and has broader applicability in other physical models with non-negativity requirements. Our study improves this method by choosing a more general solution $(u_0,v_0)$ to expand around, achieving recovery for all interaction terms. By this means, we improve on the previous results and apply this to physical models to recover coefficients concerning compression, prey attack, crowding, carrying capacity, and many other interaction factors in the system. Finally, we apply our results to study three specific cases: the hydra-effects model, the Holling-Tanner model and the classic Lotka-Volterra model.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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