Pushed-to-Pulled Front Transitions: Continuation, Speed Scalings, and Hidden Monotonicty

Pushed-to-Pulled Front Transitions: Continuation, Speed Scalings, and Hidden Monotonicty
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推拉前端转换:延续、速度缩放和隐藏单调性

DOI:
10.1007/s00332-023-09957-3
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发表时间:
2023
影响因子:
3
通讯作者:
Scheel, Arnd
Scheel, Arnd
中科院分区:
数学2区
文献类型:
--
作者:
Avery, Montie;Holzer, Matt;Scheel, Arnd

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我们从独立于模型的角度对拉锋和推锋之间的转变进行了分析和数值分析。基于最小的概念假设,我们证明了推前线从拉前线的分支中分叉,并具有有效的速度校正,该校正在分叉参数中呈二次方缩放。引人注目的是,我们发现,在没有比较原则假设的一般情况下,当前缘的单调性丢失时,拉动的前沿会失去稳定性,并让位于推动的前沿。我们的方法依赖于远场核心分解,该分解明确识别前沿前沿的渐近。我们展示了如何直接实现理论构建以产生有效的算法,该算法可以确定传播速度和分叉点,并且域大小的误差呈指数级小。这里考虑的示例应用包括扩展的 Fisher-KPP 方程、Fisher-Burgers 方程、与物流人口增长相结合的负出租车、自催化反应和 Lotka-Volterra 模型。
We analyze the transition between pulled and pushed fronts both analytically and numerically from a model-independent perspective. Based on minimal conceptual assumptions, we show that pushed fronts bifurcate from a branch of pulled fronts with an effective speed correction that scales quadratically in the bifurcation parameter. Strikingly, we find that in this general context without assumptions on comparison principles, the pulled front loses stability and gives way to a pushed front when monotonicity in the leading edge is lost. Our methods rely on far-field core decompositions that identify explicitly asymptotics in the leading edge of the front. We show how the theoretical construction can be directly implemented to yield effective algorithms that determine spreading speeds and bifurcation points with exponentially small error in the domain size. Example applications considered here include an extended Fisher-KPP equation, a Fisher–Burgers equation, negative taxis in combination with logistic population growth, an autocatalytic reaction, and a Lotka-Volterra model.
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