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arXiv 2102.08242math.NAcs.NA
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种群模型的保正方法

Positivity-preserving methods for population models

Sergio Blanes, Arieh Iserles, Shev Macnamara

摘要:

许多重要的应用都是由具有正解的微分方程来模拟的。然而,它仍然是一个悬而未决的问题,开发数值方法,都(i)的高阶精度和(ii)能够保持积极性。众所周知,数值方法的两个主要家族,龙格-库塔法和多步法,面临着一个顺序障碍:如果他们保持积极的,那么他们被限制到低精度:他们不能优于一阶。我们提出了新的方法,克服了这一障碍:我们的方法是二阶的,他们保证保持积极性。我们的方法适用于一个大类的微分方程,有一个特殊的图形拉普拉斯结构,我们阐明。方程既不需要是线性的,也不需要是自治的,并且图拉普拉斯算子不需要是对称的。这种代数结构在许多需要正性的重要应用中自然出现。我们展示了我们的新方法的应用程序,标准的高阶方法不能保持积极性,包括传染病,马尔可夫过程,主方程和化学反应。

英文摘要:

Many important applications are modelled by differential equations with positive solutions. However, it remains an outstanding open problem to develop numerical methods that are both (i) of a high order of accuracy and (ii) capable of preserving positivity. It is known that the two main families of numerical methods, Runge-Kutta methods and multistep methods, face an order barrier: if they preserve positivity, then they are constrained to low accuracy: they cannot be better than first order. We propose novel methods that overcome this barrier: our methods are of second order, and they are guaranteed to preserve positivity. Our methods apply to a large class of differential equations that have a special graph Laplacian structure, which we elucidate. The equations need be neither linear nor autonomous and the graph Laplacian need not be symmetric. This algebraic structure arises naturally in many important applications where positivity is required. We showcase our new methods on applications where standard high order methods fail to preserve positivity, including infectious diseases, Markov processes, master equations and chemical reactions.

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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