[公式:见文本]类liouville量子重力表面下,通过费曼路径积分方法研究水流对鱼类洄游的影响。

IF 1.3 4区 生物学 Q3 BIOLOGY
Theory in Biosciences Pub Date : 2021-06-01 Epub Date: 2021-05-20 DOI:10.1007/s12064-021-00345-7
Paramahansa Pramanik
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引用次数: 8

摘要

提出了一种随机微分博弈论模型来确定鱼类在河流和海洋中逆水流迁移时的最佳行为。然后,根据两个随机动力学,一个表示其位置,另一个表示其相对于水流的速度,使动态目标函数最大化。在相对速度随机动力学中,引入了白噪声下的cucker - small型随机微分方程。由于水动力环境信息的不完全和不完善,引入了[公式:见文]类liouville量子引力面下的Feynman型路径积分,得到了一个wick -旋转Schrödinger型方程,以确定鱼在洄游过程中的最优策略。费曼型路径积分的优点是,它可以用于更广义的非线性随机微分方程,在这些方程中,构造哈密顿-雅可比-贝尔曼(HJB)方程是不可能的。数学分析结果给出了不完全信息和不确定性条件下鱼的最优策略的精确表达式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effects of water currents on fish migration through a Feynman-type path integral approach under [Formula: see text] Liouville-like quantum gravity surfaces.

A stochastic differential game theoretic model has been proposed to determine optimal behavior of a fish while migrating against water currents both in rivers and oceans. Then, a dynamic objective function is maximized subject to two stochastic dynamics, one represents its location and another its relative velocity against water currents. In relative velocity stochastic dynamics, a Cucker-Smale type stochastic differential equation is introduced under white noise. As the information regarding hydrodynamic environment is incomplete and imperfect, a Feynman type path integral under [Formula: see text] Liouville-like quantum gravity surface has been introduced to obtain a Wick-rotated Schrödinger type equation to determine an optimal strategy of a fish during its migration. The advantage of having Feynman type path integral is that, it can be used in more generalized nonlinear stochastic differential equations where constructing a Hamiltonian-Jacobi-Bellman (HJB) equation is impossible. The mathematical analytic results show exact expression of an optimal strategy of a fish under imperfect information and uncertainty.

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来源期刊
Theory in Biosciences
Theory in Biosciences 生物-生物学
CiteScore
2.70
自引率
9.10%
发文量
21
审稿时长
3 months
期刊介绍: Theory in Biosciences focuses on new concepts in theoretical biology. It also includes analytical and modelling approaches as well as philosophical and historical issues. Central topics are: Artificial Life; Bioinformatics with a focus on novel methods, phenomena, and interpretations; Bioinspired Modeling; Complexity, Robustness, and Resilience; Embodied Cognition; Evolutionary Biology; Evo-Devo; Game Theoretic Modeling; Genetics; History of Biology; Language Evolution; Mathematical Biology; Origin of Life; Philosophy of Biology; Population Biology; Systems Biology; Theoretical Ecology; Theoretical Molecular Biology; Theoretical Neuroscience & Cognition.
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