Level-set method

) in 2D. Left image shows zero-level solution. Right image shows the level-set scalar field. The Level-set method (LSM) is a conceptual framework for using level sets as a tool for numerical analysis of surfaces and shapes. LSM can perform numerical computations involving curves and surfaces on a fixed Cartesian grid without having to parameterize these objects. LSM makes it easier to perform computations on shapes with sharp corners and shapes that change topology (such as by splitting in two or developing holes). These characteristics make LSM effective for modeling objects that vary in time, such as an airbag inflating or a drop of oil floating in water.

) in 2D. Left image shows zero-level solution. Right image shows the level-set scalar field.]] The Level-set method (LSM) is a conceptual framework for using level sets as a tool for numerical analysis of surfaces and shapes. LSM can perform numerical computations involving curves and surfaces on a fixed Cartesian grid without having to parameterize these objects. LSM makes it easier to perform computations on shapes with sharp corners and shapes that change topology (such as by splitting in two or developing holes). These characteristics make LSM effective for modeling objects that vary in time, such as an airbag inflating or a drop of oil floating in water. \Gamma = \{(x, y) \mid \varphi(x, y) = 0 \}, and the level-set method manipulates \Gamma implicitly through the function \varphi . This function \varphi is assumed to take positive values inside the region delimited by the curve \Gamma and negative values outside. ==The level-set equation== If the curve \Gamma moves in the normal direction with a speed v , then by chain rule and implicit differentiation, it can be determined that the level-set function \varphi satisfies the level-set equation \frac{\partial\varphi}{\partial t} = v|\nabla \varphi|. Here, |\cdot| is the Euclidean norm (denoted customarily by single bars in partial differential equations), and t is time. This is a partial differential equation, in particular a Hamilton–Jacobi equation, and can be solved numerically, for example, by using finite differences on a Cartesian grid. However, the numerical solution of the level set equation may require advanced techniques. Simple finite difference methods fail quickly. Upwinding methods such as the Godunov method are considered better; however, the level set method does not guarantee preservation of the volume and shape of the set level in an advection field that maintains shape and size, for example, a uniform or rotational velocity field. Instead, the shape of the level set may become distorted, and the level set may disappear over a few time steps. Therefore, high-order finite difference schemes, such as high-order essentially non-oscillatory (ENO) schemes, are often required, and even then, the feasibility of long-term simulations is questionable. More advanced methods have been developed to overcome this; for example, combinations of the leveling method with tracking marker particles suggested by the velocity field. ==Example== Consider a unit circle in \mathbb{R}^2 , shrinking in on itself at a constant rate, i.e. each point on the boundary of the circle moves along its inwards pointing normally at some fixed speed. The circle will shrink and eventually collapse down to a point. If an initial distance field is constructed (i.e. a function whose value is the signed Euclidean distance to the boundary, positive interior, negative exterior) on the initial circle, the normalized gradient of this field will be the circle normal. If the field has a constant value subtracted from it in time, the zero level (which was the initial boundary) of the new fields will also be circular and will similarly collapse to a point. This is due to this being effectively the temporal integration of the Eikonal equation with a fixed front velocity. ==History== The level-set method was developed in 1979 by Alain Dervieux, and subsequently popularized by Stanley Osher and James Sethian. It has since become popular in many disciplines, such as image processing, computer graphics, computational geometry, optimization, computational fluid dynamics, and computational biology. ==See also== Contour boxplot Zebra analysis G equation Advanced Simulation Library Volume of fluid method Image segmentation#Level-set methods Immersed boundary methods Stochastic Eulerian Lagrangian methods Level set (data structures) Posterization ==References== ==External links== See Ronald Fedkiw's academic web page for many pictures and animations showing how the level-set method can be used to model real-life phenomena. Multivac is a C++ library for front tracking in 2D with level-set methods. James Sethian's web page on level-set method. Stanley Osher's homepage. The Level Set Method. MIT 16.920J / 2.097J / 6.339J. Numerical Methods for Partial Differential Equations by Per-Olof Persson. March 8, 2005 Lecture 11: The Level Set Method: MIT 18.086. Mathematical Methods for Engineers II by Gilbert Strang

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