• Aucun résultat trouvé

Markov Chains on Tilings: From Chaos to Order

N/A
N/A
Protected

Academic year: 2022

Partager "Markov Chains on Tilings: From Chaos to Order"

Copied!
46
0
0

Texte intégral

(1)

From Chaos to Order

Thomas Fernique

CIRM, October 21, 2013

(2)

Order Chaos From chaos to order

1 Order

2 Chaos

3 From chaos to order

(3)

1 Order

2 Chaos

3 From chaos to order

(4)

Order Chaos From chaos to order

Quasicrystals and tilings

Definition (IUCr, 1992)

Crystal = ordered material = essentially discrete diffraction.

1982: discovery of the first non-periodic crystal ( Nobel in 2011).

The periodic crystals are usually modelled by patterns on lattices.

The non-periodic ones have quickly been modelled bytilings.

Definition

Tiling = covering of the plane by non-overlapping compact sets. Example: digitizations of affine planes in higher dimensional space. Quasicrystal stability (at lowT): finite range energetic interaction. Modelled on tilings by constraints on the way things locally fit.

(5)

Quasicrystals and tilings

Definition (IUCr, 1992)

Crystal = ordered material = essentially discrete diffraction.

1982: discovery of the first non-periodic crystal ( Nobel in 2011).

The periodic crystals are usually modelled by patterns on lattices.

The non-periodic ones have quickly been modelled bytilings.

Definition

Tiling = covering of the plane by non-overlapping compact sets.

Example: digitizations of affine planes in higher dimensional space.

Quasicrystal stability (at lowT): finite range energetic interaction.

Modelled on tilings by constraints on the way things locally fit.

(6)

Order Chaos From chaos to order

Example 1: Dimer tilings

Rows alternate rhombi (between their orientation):

(7)

Example 1: Dimer tilings

Rows alternate rhombi (between their orientation):

(8)

Order Chaos From chaos to order

Example 2: Beenker tilings

Rows alternate rhombi, squares are free:

(9)

Example 2: Beenker tilings

Rows alternate rhombi, squares are free:

(10)

Order Chaos From chaos to order

Example 2: Beenker tilings

Rows alternate rhombi, squares are free:

(11)

Example 3: generalized Penrose tilings

Rows alternate rhombi of a given type, different types freely mix:

(12)

Order Chaos From chaos to order

Example 3: generalized Penrose tilings

Rows alternate rhombi of a given type, different types freely mix:

(13)

Example 3: generalized Penrose tilings

Rows alternate rhombi of a given type, different types freely mix:

(14)

Order Chaos From chaos to order

Some references

L. S. Levitov,Local rules for quasicrystals, Comm. Math.

Phys. Volume119 (1988)

J. E. S. Socolar,Weak matching rules for quasicrystals, Comm. Math. Phys. 129(1990)

T. Q. T. Le,Local rules for quasiperiodic tilingsin The mathematics long range aperiodic order (1995)

Th. F., M. Sablik,Local rules for computable planar tilings, arXiv:1208.2759 (2012)

N. B´edaride, Th. F., When periodicities enforce aperiodicity, arXiv:1309.3686 (2013)

(15)

1 Order

2 Chaos

3 From chaos to order

(16)

Order Chaos From chaos to order

Melt and random tilings

First quasicrystals: rapid cooling of the melt (quenching).

At highT: stabilization by entropy rather than energy.

Definition (Configurational entropy of a tilingT)

S(T) := log(nb tilings of the same domain as T)/nb tiles in T.

Maximal entropy tilings? Typical properties? Random sampling?

(17)

Melt and random tilings

First quasicrystals: rapid cooling of the melt (quenching).

At highT: stabilization by entropy rather than energy.

Definition (Configurational entropy of a tilingT)

S(T) := log(nb tilings of the same domain as T)/nb tiles in T.

Maximal entropy tilings? Typical properties? Random sampling?

(18)

Order Chaos From chaos to order

Example 1: Dimer tilings

Maximal entropy tilings are planar. There are efficiently sampled.

(19)

Example 1: Dimer tilings

Maximal entropy tilings are planar. There are efficiently sampled.

(20)

Order Chaos From chaos to order

Example 1: Dimer tilings

Maximal entropy tilings are planar. There are efficiently sampled.

(21)

Example 2: Beenker tilings

Maximal entropy tilings? Typical properties? Random sampling?

(22)

Order Chaos From chaos to order

Example 2: Beenker tilings

Maximal entropy tilings? Typical properties? Random sampling?

(23)

Example 3: generalized Penrose tilings

Maximal entropy tilings? Typical properties? Random sampling?

(24)

Order Chaos From chaos to order

Example 3: generalized Penrose tilings

Maximal entropy tilings? Typical properties? Random sampling?

(25)

Some references

N. Destainville, R. Mosseri, F. Bailly,Configurational entropy of co-dimension one tilings and directed membranes, J. Stat.

Phys.87 (1997)

H. Cohn, M. Larsen, J. Propp,The Shape of a typical boxed plane partition, New-York J. Math.4(1998)

H. Cohn, R. Kenyon, J. Propp,A variational principle for domino tilings, J. Amer. Math. Soc.14(2001)

M. Widom, N. Destainville, R. Mosseri, F. Bailly,Random Tilings of High Symmetry: II. Boundary Conditions and Numerical Studies, J. Stat. Phys. 120(2005)

(26)

Order Chaos From chaos to order

1 Order

2 Chaos

3 From chaos to order

(27)

Order Chaos From chaos to order

Cooling and stochastic flips

Recent quasicrystals: slow cooling of the melt (versus quenching).

Energy minimization gradually overcomes entropy maximization.

Diffusion mechanism which makes the cooling correct the defects?

Flip on a vertexx: half-turn a hexagon of three rhombi sharingx. Diffusion: flips on random vertices with probability exp(−∆E/T). This is the Metropolis-Hastings algorithm for the Gibbs distribution

P(tiling) = 1 Z(T)exp

−E(tiling) T

.

Chaos for highT, order for lowT, but what about convergence?

(28)

Order Chaos From chaos to order

Cooling and stochastic flips

Recent quasicrystals: slow cooling of the melt (versus quenching).

Energy minimization gradually overcomes entropy maximization.

Diffusion mechanism which makes the cooling correct the defects?

Definition

Flip on a vertexx: half-turn a hexagon of three rhombi sharingx.

Diffusion: flips on random vertices with probability exp(−∆E/T).

This is the Metropolis-Hastings algorithm for the Gibbs distribution P(tiling) = 1

Z(T)exp

−E(tiling) T

.

Chaos for highT, order for lowT, but what about convergence?

(29)

Cooling and stochastic flips

Recent quasicrystals: slow cooling of the melt (versus quenching).

Energy minimization gradually overcomes entropy maximization.

Diffusion mechanism which makes the cooling correct the defects?

Definition

Flip on a vertexx: half-turn a hexagon of three rhombi sharingx.

Diffusion: flips on random vertices with probability exp(−∆E/T).

This is the Metropolis-Hastings algorithm for the Gibbs distribution P(tiling) = 1

Z(T)exp

−E(tiling) T

.

Chaos for highT, order for lowT, but what about convergence?

(30)

Order Chaos From chaos to order

Example 1: Dimer tilings

Ergodic atT >0, Θ(n2lnn) mixing atT =∞,O(n2.5) atT = 0.

(31)

Example 1: Dimer tilings

Ergodic atT >0, Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(32)

Order Chaos From chaos to order

Example 1: Dimer tilings

Ergodic atT >0, Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(33)

Example 1: Dimer tilings

Ergodic atT >0, Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(34)

Order Chaos From chaos to order

Example 1: Dimer tilings

Ergodic atT >0, Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(35)

Example 1: Dimer tilings

Ergodic atT >0, Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(36)

Order Chaos From chaos to order

Example 2: Beenker tilings

Ergodic atT >0,Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(37)

Example 2: Beenker tilings

Ergodic atT >0,Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(38)

Order Chaos From chaos to order

Example 2: Beenker tilings

Ergodic atT >0,Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(39)

Example 2: Beenker tilings

Ergodic atT >0,Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(40)

Order Chaos From chaos to order

Example 2: Beenker tilings

Ergodic atT >0,Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(41)

Example 3: generalized Penrose tilings

Ergodic atT >0,Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(42)

Order Chaos From chaos to order

Example 3: generalized Penrose tilings

Ergodic atT >0,Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(43)

Example 3: generalized Penrose tilings

Ergodic atT >0,Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(44)

Order Chaos From chaos to order

Example 3: generalized Penrose tilings

Ergodic atT >0,Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(45)

Example 3: generalized Penrose tilings

Ergodic atT >0,Θ(n2lnn) mixing atT =∞,Θ(n2) atT = 0.

(46)

Order Chaos From chaos to order

Some references

N. Destainville,Flip dynamics in octagonal rhombus tiling sets, Phys. Rev. Lett.88(2002)

D. B. Wilson, Mixing times of lozenge tiling and card shuffling Markov chains, Ann. Appl. Probab.14 (2004)

O. Bodini, Th. F., D. Regnault,Stochastic flips on two-letter words, proc. of AnAlCo (2010)

Th. F., D. Regnault, Stochastic flips on dimer tilings, Disc.

Math. Theor. Comput. Sci. (2010)

P. Caputo, F. Martinelli, F. Toninelli,Mixing times of monotone surfaces and SOS interfaces: a mean curvature approach, Comm. Math. Phys. 311 (2012)

Références

Documents relatifs

Component C: The first component is made of an East-deterministic ape- riodic set of tiles that we will call white tiles (the white background of fig- ure 4), and we add two sets

The structural results from Section 2 ensure that tilesets which allow only periodic tilings are locally robust: find n such that all the errors are in the same island of rank n

Tout comme pour les modèles de percolation de Bernoulli définis dans la Section 1.1 et le modèle de percolation de lignes nodales défini dans le Section 1.2, les

By a spectral triple for a one-sided subshift we mean the spectral triple as defined in Theorems 2.4 or 2.5 given by the above data: the tree of words, a choice of horizontal edges H

It was shown in reference [I] that, given a one-dimensional quasiperiodic substitutional structure, one should expect that the atomic surface describing its periodic order in

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des

La motivation jouant un rôle important pour que l’élève entre dans les apprentissages et persiste à y rester, cette partie va donc s’intéresser, dans un premier

Characterization and source apportionment of atmospheric organic and elemental carbon during fall and winter of 2003 in Xi’an, China... www.atmos-chem-phys.org/acp/5/3127/