Schramm–Loewner evolution

In probability theory, the Schramm–Loewner evolution with parameter κ, also known as stochastic Loewner evolution (SLE_κ), is a family of random planar curves that have been proven to be the scaling limit of a variety of two-dimensional lattice models in statistical mechanics. Given a parameter κ and a domain in the complex plane U, it gives a family of random curves in U, with κ controlling how much the curve turns. There are two main variants of SLE, chordal SLE which gives a family of random curves from two fixed boundary points, and radial SLE, which gives a family of random curves from a fixed boundary point to a fixed interior point. These curves are defined to satisfy conformal invariance and a domain Markov property.

It was discovered by Oded Schramm (2000) as a conjectured scaling limit of the planar uniform spanning tree (UST) and the planar loop-erased random walk (LERW) probabilistic processes, and developed by him together with Greg Lawler and Wendelin Werner in a series of joint papers.

Besides UST and LERW, the Schramm–Loewner evolution is conjectured or proven to describe the scaling limit of various stochastic processes in the plane, such as critical percolation, the critical Ising model, the double-dimer model, self-avoiding walks, and other critical statistical mechanics models that exhibit conformal invariance. The SLE curves are the scaling limits of interfaces and other non-self-intersecting random curves in these models. The main idea is that the conformal invariance and a certain Markov property inherent in such stochastic processes together make it possible to encode these planar curves into a one-dimensional Brownian motion running on the boundary of the domain (the driving function in Loewner's differential equation). This way, many important questions about the planar models can be translated into exercises in Itō calculus. Indeed, several mathematically non-rigorous predictions made by physicists using conformal field theory have been proven using this strategy.

The Loewner equation

Main article: Loewner differential equation

If D is a simply connected, open complex domain not equal to C, and γ is a simple curve in D starting on the boundary (a continuous function with γ(0) on the boundary of D and γ((0, ∞)) a subset of D), then for each t ≥ 0, the complement D_t of γ([0, t]) is simply connected and therefore conformally isomorphic to D by the Riemann mapping theorem. If ƒ_t is a suitable normalized isomorphism from D to D_t, then it satisfies a differential equation found by Loewner (1923, p. 121) in his work on the Bieberbach conjecture. Sometimes it is more convenient to use the inverse function g_t of ƒ_t, which is a conformal mapping from D_t to D.

In Loewner's equation, z is in the domain D, t ≥ 0, and the boundary values at time t=0 are ƒ₀(z) = z or g₀(z) = z. The equation depends on a driving function ζ(t) taking values in the boundary of D. If D is the unit disk and the curve γ is parameterized by "capacity", then Loewner's equation is

\frac{\partial f_t(z)}{\partial t} = -z f^\prime_t(z)\frac{\zeta(t)+z}{\zeta(t)-z}

\dfrac{\partial g_t(z)}{\partial t} = g_t(z)\dfrac{\zeta(t)+g_t(z)}{\zeta(t)-g_t(z)}.

When D is the upper half plane the Loewner equation differs from this by changes of variable and is

\frac{\partial f_t(z)}{\partial t} = \frac{ 2f_t^\prime(z)}{\zeta(t)-z}

\dfrac{\partial g_t(z)}{\partial t} = \dfrac{ 2}{g_t(z)-\zeta(t)}.

The driving function ζ and the curve γ are related by

\displaystyle f_t(\zeta(t)) = \gamma(t)

\displaystyle \zeta(t) = g_t(\gamma(t))

where ƒ_t and g_t are extended by continuity.

Example

If D is the upper half plane and the driving function ζ is identically zero, then

f_t(z) = \sqrt{z^2-4t}

g_t(z) = \sqrt{z^2+4t}

\gamma(t) = 2i\sqrt{t}

D_t

is the upper half plane with the line from 0 to

2i\sqrt{t}

removed.

Schramm–Loewner evolution

Schramm–Loewner evolution is the random curve γ given by the Loewner equation as in the previous section, for the driving function

\displaystyle\zeta(t)=\sqrt{\kappa}B(t)

where B(t) is Brownian motion on the boundary of D, scaled by some real κ. In other words Schramm–Loewner evolution is a probability measure on planar curves, given as the image of Wiener measure under this map.

In general the curve γ need not be simple, and the domain D_t is not the complement of γ([0,t]) in D, but is instead the unbounded component of the complement.

There are two versions of SLE, using two families of curves, each depending on a non-negative real parameter κ:

Chordal SLE_κ, which is related to curves connecting two points on the boundary of a domain (usually the upper half plane, with the points being 0 and infinity).
Radial SLE_κ, which related to curves joining a point on the boundary of a domain to a point in the interior (often curves joining 1 and 0 in the unit disk).

SLE depends on a choice of Brownian motion on the boundary of the domain, and there are several variations depending on what sort of Brownian motion is used: for example it might start at a fixed point, or start at a uniformly distributed point on the unit circle, or might have a built in drift, and so on. The parameter κ controls the rate of diffusion of the Brownian motion, and the behavior of SLE depends critically on its value.

The two domains most commonly used in Schramm–Loewner evolution are the upper half plane and the unit circle. Although the Loewner differential equation in these two cases look different, they are equivalent up to changes of variables as the unit circle and the upper half plane are conformally equivalent. However a conformal equivalence between them does not preserve the Brownian motion on their boundaries used to drive Schramm–Loewner evolution.

Special values of κ

κ = 2 corresponds to the loop-erased random walk, or equivalently, branches of the uniform spanning tree.
For κ = 8/3 SLE_κ has the restriction property and is conjectured to be the scaling limit of self-avoiding random walks. A version of it is the outer boundary of Brownian motion.
κ = 3 is the limit of interfaces for the Ising model.
For 0 ≤ κ ≤ 4 the curve γ(t) is simple (with probability 1).
κ = 4 corresponds to the path of the harmonic explorer and contour lines of the Gaussian free field.
For κ = 6 SLE_κ has the locality property. This arises in the scaling limit of critical percolation on the triangular lattice and conjecturally on other lattices.
For 4 < κ < 8 the curve γ(t) intersects itself and every point is contained in a loop but the curve is not space-filling (with probability 1).
κ = 8 corresponds to the path separating the uniform spanning tree from its dual tree.
For κ ≥ 8 the curve γ(t) is space-filling (with probability 1).

When SLE corresponds to some conformal field theory, the parameter κ is related to the central charge c of the conformal field theory by

c = \frac{(8-3\kappa)(\kappa-6)}{2\kappa}.

Each value of c < 1 corresponds to two values of κ, one value κ between 0 and 4, and a "dual" value 16/κ greater than 4.

Beffara (2008) showed that the Hausdorff dimension of the paths (with probability 1) is equal to min(2, 1 + κ/8).

Applications

Lawler, Schramm & Werner (2001) used SLE₆ to prove the conjecture of Mandelbrot (1982) that the boundary of planar Brownian motion has fractal dimension 4/3.

Critical percolation on the triangular lattice was proved to be related to SLE with κ=6 by Stanislav Smirnov.^[1] Combined with earlier work of Harry Kesten,^[2] this led to the determination of many of the critical exponents for percolation.^[3] This breakthrough, in turn, allowed further analysis of many aspects of this model.^[4]^[5]

Loop-erased random walk was shown to converge to SLE with κ=2 by Lawler, Schramm and Werner.^[6] This allowed derivation of many quantitative properties of loop-erased random walk (some of which were derived earlier by Richard Kenyon^[7]). The related random Peano curve outlining the uniform spanning tree was shown to converge to SLE with κ=8.^[6]

Rohde and Schramm showed that κ is related to the fractal dimension of a curve by the following relation

d = 1 + \frac{\kappa}{8}.

References

↑ Smirnov, Stanislav (2001). "Critical percolation in the plane". Comptes Rendus de l'Académie des Sciences 333 (3): 239–244. doi:10.1016/S0764-4442(01)01991-7.
↑ Kesten, Harry (1987). "Scaling relations for 2D-percolation". Comm. Math. Phys. 109 (1): 109–156. doi:10.1007/BF01205674.
↑ Smirnov, Stanislav; Werner, Wendelin (2001). "Critical exponents for two-dimensional percolation" (PDF). Math. Res. Lett. 8 (6): 729–744. arXiv:math/0109120v2. doi:10.4310/mrl.2001.v8.n6.a4.
↑ Schramm, Oded; Steif, Jeffrey E. (2010). "Quantitative noise sensitivity and exceptional times for percolation". Ann. of Math. 171 (2): 619–672. doi:10.4007/annals.2010.171.619.
↑ Garban, Christophe; Pete, Gábor; Schramm, Oded (2013). "Pivotal, cluster and interface measures for critical planar percolation". J. Amer. Math. Soc. 26 (4): 939–1024. doi:10.1090/S0894-0347-2013-00772-9.
1 2 Lawler, Gregory F.; Schramm, Oded; Werner, Wendelin (2004). "Conformal invariance of planar loop-erased random walks and uniform spanning trees". Ann. Probab. 32 (1B): 939–995. doi:10.1214/aop/1079021469.
↑ Kenyon, Richard (2000). "Long range properties of spanning trees". J. Math. Phys. 41 (3): 1338–1363. doi:10.1063/1.533190.

External links

Lawler; Schramm; Werner (2001), Tutorial: SLE, Lawrence Hall of Science, University of California, Berkeley ( video of MSRI lecture)
Schramm, Oded (2001), Conformally Invariant Scaling Limits and SLE, MSRI (Slides from a talk.)

Stochastic processes

Discrete time	Bernoulli process Branching process Chinese restaurant process Galton–Watson process Independent and identically distributed random variables Markov chain Moran process Random walk Loop-erased Self-avoiding

Continuous time	Bessel process Birth–death process Brownian motion Bridge Excursion Fractional Geometric Meander Cauchy process Contact process Continuous-time random walk Cox process Diffusion process Empirical process Feller process Fleming–Viot process Gamma process Hunt process Interacting particle systems Itô diffusion Itô process Jump diffusion Jump process Lévy process Local time Markov additive process McKean–Vlasov process Ornstein–Uhlenbeck process Poisson process Compound Non-homogeneous Point process Schramm–Loewner evolution Semimartingale Sigma-martingale Stable process Superprocess Telegraph process Variance gamma process Wiener process Wiener sausage

Both	Branching process Gaussian process Hidden Markov model (HMM) Markov process Martingale Differences Local Sub- Super- Random dynamical system Regenerative process Renewal process White noise

Fields and other	Dirichlet process Gaussian random field Gibbs measure Hopfield model Ising model Potts model Boolean network Markov random field Percolation Pitman–Yor process Point process Cox Poisson Random field Random graph

Time series models	Autoregressive conditional heteroskedasticity (ARCH) model Autoregressive integrated moving average (ARIMA) model Autoregressive (AR) model Autoregressive–moving-average (ARMA) model Generalized autoregressive conditional heteroskedasticity (GARCH) model Moving-average (MA) model

Financial models	Black–Derman–Toy Black–Karasinski Black–Scholes Chen Constant elasticity of variance (CEV) Cox–Ingersoll–Ross (CIR) Garman–Kohlhagen Heath–Jarrow–Morton (HJM) Heston Ho–Lee Hull–White LIBOR market Rendleman–Bartter SABR volatility Vašíček Wilkie

Actuarial models	Bühlmann Cramér–Lundberg Risk process Sparre–Anderson

Queueing models	Bulk Fluid Generalized queueing network M/G/1 M/M/1 M/M/c

Properties	Càdlàg paths Continuous Continuous paths Ergodic Exchangeable Feller-continuous Gauss–Markov Markov Mixing Piecewise deterministic Predictable Progressively measurable Self-similar Stationary Time-reversible

Limit theorems	Central limit theorem Donsker's theorem Doob's martingale convergence theorems Ergodic theorem Fisher–Tippett–Gnedenko theorem Large deviation principle Law of large numbers (weak/strong) Law of the iterated logarithm Maximal ergodic theorem Sanov's theorem

Inequalities	Burkholder–Davis–Gundy Doob's martingale Kunita–Watanabe

Tools	Cameron–Martin formula Convergence of random variables Doléans-Dade exponential Doob decomposition theorem Doob–Meyer decomposition theorem Doob's optional stopping theorem Dynkin's formula Feynman–Kac formula Filtration Girsanov theorem Infinitesimal generator Itô integral Itô's lemma Kolmogorov continuity theorem Kolmogorov extension theorem Lévy–Prokhorov metric Malliavin calculus Martingale representation theorem Optional stopping theorem Prokhorov's theorem Quadratic variation Reflection principle Skorokhod integral Skorokhod's representation theorem Skorokhod space Snell envelope Stochastic differential equation Tanaka Stopping time Stratonovich integral Uniform integrability Usual hypotheses Wiener space Classical Abstract

Disciplines	Actuarial mathematics Econometrics Ergodic theory Extreme value theory (EVT) Large deviations theory Mathematical finance Mathematical statistics Probability theory Queueing theory Renewal theory Ruin theory Statistics Stochastic analysis Time series analysis Machine learning

List of topics Category

This article is issued from Wikipedia - version of the Tuesday, July 28, 2015. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.

Schramm–Loewner evolution

The Loewner equation

Example

Schramm–Loewner evolution

Special values of κ

Applications

References

Further reading

External links