An eikonal equation (from Greek εἰκών, image[1][2]) is a non-linear first-order partial differential equation that is encountered in problems of wave propagation.

The classical eikonal equation in geometric optics is a differential equation of the form

(1)

where lies in an open subset of , is a positive function, denotes the gradient, and is the Euclidean norm. The function is given and one seeks solutions . In the context of geometric optics, the function is the refractive index of the medium.

More generally, an eikonal equation is an equation of the form

(2)

where is a function of variables. Here the function is given, and is the solution. If , then equation (2) becomes (1).

Eikonal equations naturally arise in the WKB method[3] and the study of Maxwell's equations.[4] Eikonal equations provide a link between physical (wave) optics and geometric (ray) optics.

One fast computational algorithm to approximate the solution to the eikonal equation is the fast marching method.

History

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The term "eikonal" was first used in the context of geometric optics by Heinrich Bruns.[5] However, the actual equation appears earlier in the seminal work of William Rowan Hamilton on geometric optics.[6]

Physical interpretation

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Continuous shortest-path problems

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Suppose that   is an open set with suitably smooth boundary  . The solution to the eikonal equation

 
 

can be interpreted as the minimal amount of time required to travel from   to  , where   is the speed of travel, and   is an exit-time penalty. (Alternatively this can be posed as a minimal cost-to-exit by making the right-side   and   an exit-cost penalty.)

In the special case when  , the solution gives the signed distance from  .[7]

By assuming that   exists at all points, it is easy to prove that   corresponds to a time-optimal control problem using Bellman's optimality principle and a Taylor expansion.[8] Unfortunately, it is not guaranteed that   exists at all points, and more advanced techniques are necessary to prove this. This led to the development of viscosity solutions in the 1980s by Pierre-Louis Lions and Michael G. Crandall,[9] and Lions won a Fields Medal for his contributions.

Electromagnetic potential

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The physical meaning of the eikonal equation is related to the formula

 

where   is the electric field strength, and   is the electric potential. There is a similar equation for velocity potential in fluid flow and temperature in heat transfer. The physical meaning of this equation in the electromagnetic example is that any charge in the region is pushed to move at right angles to the lines[clarification needed] of constant potential, and along lines of force determined by the field of the E vector and the sign of the charge.

Ray optics and electromagnetism are related by the fact that the eikonal equation gives a second electromagnetic formula of the same form as the potential equation above where the line of constant potential has been replaced by a line of constant phase, and the force lines have been replaced by normal vectors coming out of the constant phase line at right angles. The magnitude of these normal vectors is given by the square root of the relative permittivity. The line of constant phase can be considered the edge of one of the advancing light waves (wavefront). The normal vectors are the rays the light is traveling down in ray optics.

Computational algorithms

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Several fast and efficient algorithms to solve the eikonal equation have been developed since the 1990s. Many of these algorithms take advantage of algorithms developed much earlier for shortest path problems on graphs with nonnegative edge lengths.[10] These algorithms take advantage of the causality provided by the physical interpretation and typically discretize the domain using a mesh[11][12][13][14] or regular grid[15][16] and calculate the solution at each discretized point. Eikonal solvers on triangulated surfaces were introduced by Kimmel and Sethian in 1998.[11][12]

Sethian's fast marching method (FMM)[15][16] was the first "fast and efficient" algorithm created to solve the Eikonal equation. The original description discretizes the domain   into a regular grid and "marches" the solution from "known" values to the undiscovered regions, precisely mirroring the logic of Dijkstra's algorithm. If   is discretized and has   meshpoints, then the computational complexity is   where the   term comes from the use of a heap (typically binary). A number of modifications can be prescribed to FMM since it is classified as a label-setting method. In addition, FMM has been generalized to operate on general meshes that discretize the domain.[11][12][13][14]

Label-correcting methods such as the Bellman–Ford algorithm can also be used to solve the discretized Eikonal equation also with numerous modifications allowed (e.g. "Small Labels First" [10][17] or "Large Labels Last" [10][18]). Two-queue methods have also been developed[19] that are essentially a version of the Bellman-Ford algorithm except two queues are used with a threshold used to determine which queue a gridpoint should be assigned to based on local information.

Sweeping algorithms such as the fast sweeping method (FSM)[20] are highly efficient for solving Eikonal equations when the corresponding characteristic curves do not change direction very often.[10] These algorithms are label-correcting but do not make use of a queue or heap, and instead prescribe different orderings for the gridpoints to be updated and iterate through these orderings until convergence. Some improvements were introduced such as "locking" gridpoints[19] during a sweep if does not receive an update, but on highly refined grids and higher-dimensional spaces there is still a large overhead due to having to pass through every gridpoint. Parallel methods have been introduced that attempt to decompose the domain and perform sweeping on each decomposed subset. Zhao's parallel implementation decomposes the domain into  -dimensional subsets and then runs an individual FSM on each subset.[21] Detrixhe's parallel implementation also decomposes the domain, but parallelizes each individual sweep so that processors are responsible for updating gridpoints in an  -dimensional hyperplane until the entire domain is fully swept.[22]

Hybrid methods have also been introduced that take advantage of FMM's efficiency with FSM's simplicity. For example, the Heap Cell Method (HCM) decomposes the domain into cells and performs FMM on the cell-domain, and each time a "cell" is updated FSM is performed on the local gridpoint-domain that lies within that cell.[10] A parallelized version of HCM has also been developed.[23]

Numerical approximation

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For simplicity assume that   is discretized into a uniform grid with spacings   and   in the x and y directions, respectively.

2D approximation on a Cartesian grid

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Assume that a gridpoint   has value  . A first-order scheme to approximate the partial derivatives is

 

where

 

Due to the consistent, monotone, and causal properties of this discretization[10] it is easy to show that if   and   and   then

 

which can be solved as a quadratic. In the limiting case of  , this reduces to

 

This solution will always exist as long as   is satisfied and is larger than both,   and  , as long as   .

If  , a lower-dimensional update must be performed by assuming one of the partial derivatives is  :

 

n-D approximation on a Cartesian grid

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Assume that a grid point   has value  . Repeating the same steps as in the   case we can use a first-order scheme to approximate the partial derivatives. Let   be the minimum of the values of the neighbors in the   directions, where   is a standard unit basis vector. The approximation is then

 

Solving this quadratic equation for   yields:

 

If the discriminant in the square root is negative, then a lower-dimensional update must be performed (i.e. one of the partial derivatives is  ).

If   then perform the one-dimensional update

 

If   then perform an   dimensional update using the values   for every   and choose the smallest.

Mathematical description

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An eikonal equation is one of the form

 
 

The plane   can be thought of as the initial condition, by thinking of   as   We could also solve the equation on a subset of this plane, or on a curved surface, with obvious modifications.

The eikonal equation shows up in geometrical optics, which is a way of studying solutions of the wave equation  , where   and  . In geometric optics, the eikonal equation describes the phase fronts of waves. Under reasonable hypothesis on the "initial" data, the eikonal equation admits a local solution, but a global smooth solution (e.g. a solution for all time in the geometrical optics case) is not possible. The reason is that caustics may develop. In the geometrical optics case, this means that wavefronts cross.

We can solve the eikonal equation using the method of characteristics. One must impose the "non-characteristic" hypothesis   along the initial hypersurface  , where H = H(x,p) and p = (p1,...,pn) is the variable that gets replaced by ∇u. Here x = (x1,...,xn) = (t,x′).

First, solve the problem  ,  . This is done by defining curves (and values of   on those curves) as

 
  Note that even before we have a solution  , we know   for   due to our equation for  .

That these equations have a solution for some interval   follows from standard ODE theorems (using the non-characteristic hypothesis). These curves fill out an open set around the plane  . Thus the curves define the value of   in an open set about our initial plane. Once defined as such it is easy to see using the chain rule that  , and therefore   along these curves.

We want our solution   to satisfy  , or more specifically, for every  ,   Assuming for a minute that this is possible, for any solution   we must have

 

and therefore

 

In other words, the solution   will be given in a neighborhood of the initial plane by an explicit equation. However, since the different paths  , starting from different initial points may cross, the solution may become multi-valued, at which point we have developed caustics. We also have (even before showing that   is a solution)

 

It remains to show that  , which we have defined in a neighborhood of our initial plane, is the gradient of some function  . This will follow if we show that the vector field   is curl free. Consider the first term in the definition of  . This term,   is curl free as it is the gradient of a function. As for the other term, we note

 

The result follows.

Applications

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See also

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References

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  1. ^ The Oxford English Dictionary. 2nd ed. 1989. OED Online. Oxford University Press. 4 April 2000 http://dictionary.oed.com/cgi/entry/00292404
  2. ^ Evans, L. C. Partial Differential Equations. AMS Graduate Texts in Mathematics. Vol. 19. p. 93.
  3. ^ Dimassi, Mouez; Sjöstrand, Johannes (1999). Spectral asymptotics in the semi-classical limit. London Math. Society Lecture Notes 268. Cambridge University Press. ISBN 0-521-66544-2.
  4. ^ Rauch, Jeffrey (2012), Hyperbolic partial differential equations and geometric optics, Graduate Studies in Mathematics, 133, American Mathematical Society, Bibcode:2012hpde.book.....R, ISBN 978-0-8218-7291-8
  5. ^ Bruns, Heinrich (1895). Das Eikonal. S. Hirzel.
  6. ^ Hamilton, William Rowan (1828). "Theory of Systems of Rays". Transactions of the Royal Irish Academy. 15: 69–174.
  7. ^ Sakai, Takashi. "On Riemannian manifolds admitting a function whose gradient is of constant norm." Kodai Mathematical Journal 19.1 (1996): 39-51.
  8. ^ Clawson, Z.; Chacon, A.; Vladimirsky, A. (2014). "Causal Domain Restriction for Eikonal Equations". SIAM Journal on Scientific Computing. 36 (5): A2478–A2505. arXiv:1309.2884. Bibcode:2014SJSC...36A2478C. doi:10.1137/130936531. S2CID 17226196.
  9. ^ Bardi, M.; Capuzzo-Dolcetta, I. (1997). Optimal Control and Viscosity Solutions of Hamilton–Jacobi–Bellman Equations. Boston: Birkhäuser. ISBN 0-8176-3640-4.
  10. ^ a b c d e f Chacon, A.; Vladimirsky, A. (2012). "Fast two-scale methods for eikonal equations". SIAM Journal on Scientific Computing. 34 (2): A547–A578. arXiv:1110.6220. Bibcode:2012SJSC...34A.547C. doi:10.1137/10080909X. S2CID 6404391.
  11. ^ a b c Kimmel, R.; Sethian, J. A. (1998). "Computing Geodesic Paths on Manifolds". Proceedings of the National Academy of Sciences. 95 (15): 8431–8435. Bibcode:1998PNAS...95.8431K. doi:10.1073/pnas.95.15.8431. PMC 21092. PMID 9671694.
  12. ^ a b c Bronstein, A. M.; Bronstein, M. M.; Kimmel, R. (2007). "Weighted distance maps computation on parametric three-dimensional manifolds". Journal of Computational Physics. 225 (1): 771–784. Bibcode:2007JCoPh.225..771B. doi:10.1016/j.jcp.2007.01.009.
  13. ^ a b Sethian, J. A.; Vladimirsky, A. (2000). "Fast methods for the Eikonal and related Hamilton–Jacobi equations on unstructured meshes". Proc. Natl. Acad. Sci. USA. 97 (11): 5699–5703. Bibcode:2000PNAS...97.5699S. doi:10.1073/pnas.090060097. PMC 18495. PMID 10811874.
  14. ^ a b Yershov, D. S.; LaValle, S. M. (2012). "Simplicial Dijkstra and A* Algorithms: From Graphs to Continuous Spaces". Advanced Robotics. 26 (17): 2065–2085. doi:10.1080/01691864.2012.729559. S2CID 17573584.
  15. ^ a b Sethian, J. A. (1996). "A Fast Marching Level Set Method for Monotonically Advancing Fronts". Proc. Natl. Acad. Sci. 93 (4): 1591–1595. Bibcode:1996PNAS...93.1591S. doi:10.1073/pnas.93.4.1591. PMC 39986. PMID 11607632.
  16. ^ a b Tsitsiklis, J. N. (1995). "Efficient algorithms for globally optimal trajectories". IEEE Trans. Autom. Control. 40 (9): 1528–1538. doi:10.1109/9.412624. hdl:1721.1/3340.
  17. ^ Bertsekas, D. P. (1993). "A Simple and Fast Label Correcting Algorithm for Shortest Paths". Networks. 23 (8): 703–709. doi:10.1002/net.3230230808. hdl:1721.1/3256.
  18. ^ Bertsekas, D. P.; Guerriero, F.; Musmanno, R. (1996). "Parallel Asynchronous Label Correcting Methods for Shortest Paths". Journal of Optimization Theory and Applications. 88 (2): 297–320. doi:10.1007/BF02192173. hdl:1721.1/3390. S2CID 13172492.
  19. ^ a b Bak, S.; McLaughlin, J.; Renzi, D. (2010). "Some improvements for the fast sweeping method". SIAM Journal on Scientific Computing. 32 (5): 2853–2874. Bibcode:2010SJSC...32.2853B. doi:10.1137/090749645.
  20. ^ Zhao, H. (2004). "A fast sweeping method for eikonal equations". Math. Comp. 74 (250): 603–627. doi:10.1090/S0025-5718-04-01678-3.
  21. ^ Zhao, H. (2007). "Parallel Implementations of the Fast Sweeping Method". J. Comput. Math. 25 (4): 421–429. JSTOR 43693378.
  22. ^ Detrixhe, M.; Gibou, F.; Min, C. (2013). "A parallel fast sweeping method for the Eikonal equation". Journal of Computational Physics. 237: 46–55. Bibcode:2013JCoPh.237...46D. doi:10.1016/j.jcp.2012.11.042.
  23. ^ Chacon, A.; Vladimirsky, A. (2015). "A parallel two-scale method for Eikonal equations". SIAM Journal on Scientific Computing. 37 (1): A156–A180. arXiv:1306.4743. Bibcode:2015SJSC...37A.156C. doi:10.1137/12088197X.

Further reading

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