论文标题
用小吸收陷阱在2-D中的近盘和椭圆形域中的平均第一通道时间优化
Optimization of the Mean First Passage Time in Near-Disk and Elliptical Domains in 2-D with Small Absorbing Traps
论文作者
论文摘要
在包含小吸收陷阱的有界的二维结构域中,布朗粒子的平均第一次传递时间(MFPT)的确定是生物物理应用的基本问题。假设随机步行的起点分布均匀分布,平均MFPT是预期的捕获时间。我们开发了一种混合渐近学数方法,以预测$ m $的固定圆形吸收陷阱的最佳配置,从而最大程度地减少近盘和椭圆形域中的平均MFPT。对于一类近盘域的一类,我们通过几个特定示例说明了如何使用简单但高度准确的数值方法来实施渐近理论。从针对Neumann Green功能的新明确公式及其椭圆的常规部分推导,基于我们的渐近理论的数值方法可用于研究最佳陷阱位置的空间分布如何随着固定区域椭圆形的宽高比的变化而变化。将椭圆的混合理论的结果与从最接近点方法\ cite {iwwc2019}计算出的完整PDE数值结果进行了比较。对于长而薄的椭圆形,可以证明$ M = 2,\ ldots的最佳陷阱图案,沿椭圆形的半高轴沿5 $相同的陷阱。对于这种本质上是1-D模式,制定并实施了薄域渐近分析,以准确预测共线陷阱模式的最佳位置和相应的最佳平均MFPT。
The determination of the mean first passage time (MFPT) for a Brownian particle in a bounded 2-D domain containing small absorbing traps is a fundamental problem with biophysical applications. The average MFPT is the expected capture time assuming a uniform distribution of starting points for the random walk. We develop a hybrid asymptotic-numerical approach to predict optimal configurations of $m$ small stationary circular absorbing traps that minimize the average MFPT in near-disk and elliptical domains. For a general class of near-disk domains, we illustrate through several specific examples how simple, but yet highly accurate, numerical methods can be used to implement the asymptotic theory. From the derivation of a new explicit formula for the Neumann Green's function and its regular part for the ellipse, a numerical approach based on our asymptotic theory is used to investigate how the spatial distribution of the optimal trap locations changes as the aspect ratio of an ellipse of fixed area is varied. The results from the hybrid theory for the ellipse are compared with full PDE numerical results computed from the closest point method \cite{IWWC2019}. For long and thin ellipses, it is shown that the optimal trap pattern for $m=2,\ldots,5$ identical traps is collinear along the semi-major axis of the ellipse. For such essentially 1-D patterns, a thin-domain asymptotic analysis is formulated and implemented to accurately predict the optimal locations of collinear trap patterns and the corresponding optimal average MFPT.