标题： 随机增长方程和参数重置不变性 显示英文标题

标题： Stochastic Growth Equations and Reparametrization Invariance

摘要： 通过施加参数重置不变性，可以推导出描述表面生长动力学的各种随机方程，并确定各种项的物理过程。这种方法为获得界面的连续生长方程提供了一种特别清晰的方式。很容易推导出描述离散格点模型粗粒度演化的方程，并分析其小梯度展开。通过这种方式，作者确定了导致最常用生长方程的基本机制。这种生长过程表述的优势在于它允许超越经常使用的无悬垂近似。参数重置不变形式还明确显示了特定过程的守恒定律以及通常在小梯度展开中丢失的空间时间变换的所有对称性。最后，观察到，除了最低阶梯度展开之外，对完整运动方程的了解可能在通常微扰重整化方法失效的问题中是相关的。 显示英文摘要

摘要： It is shown that, by imposing reparametrization invariance, one may derive a variety of stochastic equations describing the dynamics of surface growth and identify the physical processes responsible for the various terms. This approach provides a particularly transparent way to obtain continuum growth equations for interfaces. It is straightforward to derive equations which describe the coarse grained evolution of discrete lattice models and analyze their small gradient expansion. In this way, the authors identify the basic mechanisms which lead to the most commonly used growth equations. The advantages of this formulation of growth processes is that it allows one to go beyond the frequently used no-overhang approximation. The reparametrization invariant form also displays explicitly the conservation laws for the specific process and all the symmetries with respect to space-time transformations which are usually lost in the small gradient expansion. Finally, it is observed, that the knowledge of the full equation of motion, beyond the lowest order gradient expansion, might be relevant in problems where the usual perturbative renormalization methods fail.