Regularity Structures

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1 Regularity Structures Martin Hairer University of Warwick Seoul, August 14, 2014

2 What are regularity structures? Algebraic structures providing skeleton for analytical models mimicking properties of Taylor polynomials: (T, G, A). Model: T R d D. Polynomial model: (P, x 0 ) P ( x 0 ). Algebraic properties: Group G acting by reexpansions on P T : P (x x 0 ) = P ((x x 1 ) + x 1 x 0 ) = ( Γ x0,x 1 P ) (x x 1 ). For every Γ G, deg(γp P ) < deg P and ΓP Q = (ΓP )(ΓQ). Analytical properties: Homogeneous monomials vanish at base point with order (speed) equal to their degree.

3 What are regularity structures? Algebraic structures providing skeleton for analytical models mimicking properties of Taylor polynomials: (T, G, A). Model: T R d D. Polynomial model: (P, x 0 ) P ( x 0 ). Algebraic properties: Group G acting by reexpansions on P T : P (x x 0 ) = P ((x x 1 ) + x 1 x 0 ) = ( Γ x0,x 1 P ) (x x 1 ). For every Γ G, deg(γp P ) < deg P and ΓP Q = (ΓP )(ΓQ). Analytical properties: Homogeneous monomials vanish at base point with order (speed) equal to their degree.

4 Another example T : linear span of 1 (degree 0) and W (degree 1 2 ). Model: For some fixed Hölder- 1 2 function W, set (a1 + bw, x 0 ) a + b ( W ( ) W (x 0 ) ). Group G: Γ x0,x 1 W = W + ( W (x 0 ) W (x 1 ) ) 1. Γ x0,x 1 1 = 1 Important property: For a given regularity structure, one can have many different models. (Here: given by choice of W.)

5 Another example T : linear span of 1 (degree 0) and W (degree 1 2 ). Model: For some fixed Hölder- 1 2 function W, set (a1 + bw, x 0 ) a + b ( W ( ) W (x 0 ) ). Group G: Γ x0,x 1 W = W + ( W (x 0 ) W (x 1 ) ) 1. Γ x0,x 1 1 = 1 Important property: For a given regularity structure, one can have many different models. (Here: given by choice of W.)

6 What are they good for? Construct robust solution theories for very singular SPDEs. Examples: t h = 2 xh + ( x h) 2 + ξ, (d = 1) t Φ = Φ Φ 3 + ξ, (d = 2, 3) t u = u + g ij (u) i u j u + σ(u)η, (d = 2, 3) t v = 2 xv + f(v) + σ(v)ξ. (d = 1) Here ξ is space-time white noise and η is spatial white noise. KPZ (h): universal model for interface propagation. Dynamical Φ 4 3 : universal model for dynamics of near mean-field phase transition models near critical temperature. PAM (u with g = 0 and σ(u) = u): universal model for weakly killed diffusions.

7 What are they good for? Construct robust solution theories for very singular SPDEs. Examples: t h = 2 xh + ( x h) 2 + ξ, (d = 1) t Φ = Φ Φ 3 + ξ, (d = 2, 3) t u = u + g ij (u) i u j u + σ(u)η, (d = 2, 3) t v = 2 xv + f(v) + σ(v)ξ. (d = 1) Here ξ is space-time white noise and η is spatial white noise. KPZ (h): universal model for interface propagation. Dynamical Φ 4 3 : universal model for dynamics of near mean-field phase transition models near critical temperature. PAM (u with g = 0 and σ(u) = u): universal model for weakly killed diffusions.

8 What are they good for? Construct robust solution theories for very singular SPDEs. Examples: R t h = xh 2 + ( x h) 2 + ξ, S A (d = 1) F M t Φ = Φ Φ 3 C α D γ + ξ, (d = 2, 3) t u = u + Ψg ij (u) i u j u + σ(u)η, (d = R 2, 3) t v = xv 2 + f(v) + σ(v)ξ. S C (d = 1) F? C α C α Here ξ is space-time white noise and η is spatial white noise. R Noise I.C. KPZ (h): universal model for interface propagation. Dynamical Φ 4 3 : universal model for dynamics of near mean-field phase Cf. A Theory of Regularity Structures (H. 14) transition models near critical temperature. PAM (u with g = 0 and σ(u) = u): universal model for weakly killed diffusions.

9 Example of renormalisation Try to define distribution η(x) = 1 x Cδ(x). Problem: Integral of 1/ x diverges, so we need to set C = to compensate! Formal definition: η χ (φ) = R φ(x) χ(x)φ(0) x dx, for some smooth compactly supported cut-off χ with χ(0) = 1. Yields one-parameter family c η c of models, but no canonical choice of origin for c. Approximation: 1/(ε + x ) 2 log ε δ(x) converges to η c for some c.

10 Example of renormalisation Try to define distribution η(x) = 1 x Cδ(x). Problem: Integral of 1/ x diverges, so we need to set C = to compensate! Formal definition: η χ (φ) = R φ(x) χ(x)φ(0) x dx, for some smooth compactly supported cut-off χ with χ(0) = 1. Yields one-parameter family c η c of models, but no canonical choice of origin for c. Approximation: 1/(ε + x ) 2 log ε δ(x) converges to η c for some c.

11 Example of renormalisation Try to define distribution η(x) = 1 x Cδ(x). Problem: Integral of 1/ x diverges, so we need to set C = to compensate! Formal definition: η χ (φ) = R φ(x) χ(x)φ(0) x dx, for some smooth compactly supported cut-off χ with χ(0) = 1. Yields one-parameter family c η c of models, but no canonical choice of origin for c. Approximation: 1/(ε + x ) 2 log ε δ(x) converges to η c for some c.

12 Example of renormalisation Try to define distribution η(x) = 1 x Cδ(x). Problem: Integral of 1/ x diverges, so we need to set C = to compensate! Formal definition: η χ (φ) = R φ(x) χ(x)φ(0) x dx, for some smooth compactly supported cut-off χ with χ(0) = 1. Yields one-parameter family c η c of models, but no canonical choice of origin for c. Approximation: 1/(ε + x ) 2 log ε δ(x) converges to η c for some c.

13 Previous notions of solution 1. If nonlinear term is σ(u) ξ, Itô calculus can be used. Relies crucially on martingale property, broken by regularisation. 2. KPZ and 1D stochastic Burgers can be treated using controlled rough paths by Lyons / Gubinelli (H. 11 / H. 13). 3. Solve t Z = 2 xz + Z ξ (SHE) and interpret h = log Z as KPZ (Hopf 50 / Cole 51 / Bertini-Giacomin 97). 4. Dynamical Φ 4 2 model: write Φ = Ψ + Φ with Ψ solution to linear equation and derive well-posed equation for Φ (Albeverio-Röckner 91 / Da Prato-Debussche 03). 5. Alternative theory using paraproducts can in principle treat KPZ and Φ 4 3 (Gubinelli-Imkeller-Perkowski 14).

14 Previous notions of solution 1. If nonlinear term is σ(u) ξ, Itô calculus can be used. Relies crucially on martingale property, broken by regularisation. 2. KPZ and 1D stochastic Burgers can be treated using controlled rough paths by Lyons / Gubinelli (H. 11 / H. 13). 3. Solve t Z = 2 xz + Z ξ (SHE) and interpret h = log Z as KPZ (Hopf 50 / Cole 51 / Bertini-Giacomin 97). 4. Dynamical Φ 4 2 model: write Φ = Ψ + Φ with Ψ solution to linear equation and derive well-posed equation for Φ (Albeverio-Röckner 91 / Da Prato-Debussche 03). 5. Alternative theory using paraproducts can in principle treat KPZ and Φ 4 3 (Gubinelli-Imkeller-Perkowski 14).

15 Previous notions of solution 1. If nonlinear term is σ(u) ξ, Itô calculus can be used. Relies crucially on martingale property, broken by regularisation. 2. KPZ and 1D stochastic Burgers can be treated using controlled rough paths by Lyons / Gubinelli (H. 11 / H. 13). 3. Solve t Z = 2 xz + Z ξ (SHE) and interpret h = log Z as KPZ (Hopf 50 / Cole 51 / Bertini-Giacomin 97). 4. Dynamical Φ 4 2 model: write Φ = Ψ + Φ with Ψ solution to linear equation and derive well-posed equation for Φ (Albeverio-Röckner 91 / Da Prato-Debussche 03). 5. Alternative theory using paraproducts can in principle treat KPZ and Φ 4 3 (Gubinelli-Imkeller-Perkowski 14).

16 Previous notions of solution 1. If nonlinear term is σ(u) ξ, Itô calculus can be used. Relies crucially on martingale property, broken by regularisation. 2. KPZ and 1D stochastic Burgers can be treated using controlled rough paths by Lyons / Gubinelli (H. 11 / H. 13). 3. Solve t Z = 2 xz + Z ξ (SHE) and interpret h = log Z as KPZ (Hopf 50 / Cole 51 / Bertini-Giacomin 97). 4. Dynamical Φ 4 2 model: write Φ = Ψ + Φ with Ψ solution to linear equation and derive well-posed equation for Φ (Albeverio-Röckner 91 / Da Prato-Debussche 03). 5. Alternative theory using paraproducts can in principle treat KPZ and Φ 4 3 (Gubinelli-Imkeller-Perkowski 14).

17 Previous notions of solution 1. If nonlinear term is σ(u) ξ, Itô calculus can be used. Relies crucially on martingale property, broken by regularisation. 2. KPZ and 1D stochastic Burgers can be treated using controlled rough paths by Lyons / Gubinelli (H. 11 / H. 13). 3. Solve t Z = 2 xz + Z ξ (SHE) and interpret h = log Z as KPZ (Hopf 50 / Cole 51 / Bertini-Giacomin 97). 4. Dynamical Φ 4 2 model: write Φ = Ψ + Φ with Ψ solution to linear equation and derive well-posed equation for Φ (Albeverio-Röckner 91 / Da Prato-Debussche 03). 5. Alternative theory using paraproducts can in principle treat KPZ and Φ 4 3 (Gubinelli-Imkeller-Perkowski 14).

18 Universality Central limit theorem: Gaussian universality KPZ strong Universality conjecture: At large scales, the space-time fluctuations of a large class of dimensional interface propagation model are described by a universal Markov process H, self-similar with exponents 1 2 3: λ 1 H(λ 2 x, λ 3 t) law = H(x, t). Exactly solvable models: Borodin, Corwin, Quastel, Sasamoto, Spohn, etc. Yields partial characterisation of limiting KPZ fixed point (H): agrees with experimental evidence (Takeuchi & Al).

19 Universality Central limit theorem: Gaussian universality KPZ strong Universality conjecture: At large scales, the space-time fluctuations of a large class of dimensional interface propagation model are described by a universal Markov process H, self-similar with exponents 1 2 3: λ 1 H(λ 2 x, λ 3 t) law = H(x, t). Exactly solvable models: Borodin, Corwin, Quastel, Sasamoto, Spohn, etc. Yields partial characterisation of limiting KPZ fixed point (H): agrees with experimental evidence (Takeuchi & Al).

20 Universality Central limit theorem: Gaussian universality KPZ strong Universality conjecture: At large scales, the space-time fluctuations of a large class of dimensional interface propagation model are described by a universal Markov process H, self-similar with exponents 1 2 3: λ 1 H(λ 2 x, λ 3 t) law = H(x, t). Exactly solvable models: Borodin, Corwin, Quastel, Sasamoto, Spohn, etc. Yields partial characterisation of limiting KPZ fixed point (H): agrees with experimental evidence (Takeuchi & Al).

21 Universality Central limit theorem: Gaussian universality KPZ strong Universality conjecture: At large scales, the space-time fluctuations of a large class of dimensional interface propagation model are described by a universal Markov process H, self-similar with exponents 1 2 3: λ 1 H(λ2 x, λ3 t) = H(x, t). law Exactly solvable models: Borodin, Corwin, Quastel, Sasamoto, Spohn, etc. Yields partial characterisation of limiting KPZ fixed point (H): agrees with experimental evidence (Takeuchi & Al).

22 Heuristic picture Universality for symmetric interface fluctuation models: scaling exponents 1 2 4, Gaussian limit. Heuristic picture of the evolution of interface models under zooming out : Gauss KPZ KPZ equation: red line.

23 Heuristic picture Universality for symmetric interface fluctuation models: scaling exponents 1 2 4, Gaussian limit. Heuristic picture of the evolution of interface models under zooming out : Gauss KPZ KPZ KPZ equation: red line.

24 Weak Universality conjecture Conjecture: the KPZ equation is the only model on the red line. Conjecture: Let h ε be any natural one-parameter family of asymmetric interface models with ε denoting the strength of the asymmetry such that propagation speed ε. As ε 0, there is a choice of C ε ε 1 such that εhε (ε 1 x, ε 2 t) C ε t converges to solutions h to the KPZ equation. Height function of WASEP (Bertini-Giacomin 97). Accumulation points satisfy weak version of KPZ for certain generalisations of WASEP (Jara-Gonçalves 10).

25 Weak Universality conjecture Conjecture: the KPZ equation is the only model on the red line. Conjecture: Let h ε be any natural one-parameter family of asymmetric interface models with ε denoting the strength of the asymmetry such that propagation speed ε. As ε 0, there is a choice of C ε ε 1 such that εhε (ε 1 x, ε 2 t) C ε t converges to solutions h to the KPZ equation. Height function of WASEP (Bertini-Giacomin 97). Accumulation points satisfy weak version of KPZ for certain generalisations of WASEP (Jara-Gonçalves 10).

26 Weak Universality conjecture Conjecture: the KPZ equation is the only model on the red line. Conjecture: Let h ε be any natural one-parameter family of asymmetric interface models with ε denoting the strength of the asymmetry such that propagation speed ε. As ε 0, there is a choice of C ε ε 1 such that εhε (ε 1 x, ε 2 t) C ε t converges to solutions h to the KPZ equation. Height function of WASEP (Bertini-Giacomin 97). Accumulation points satisfy weak version of KPZ for certain generalisations of WASEP (Jara-Gonçalves 10).

27 Weak Universality conjecture Conjecture: the KPZ equation is the only model on the red line. Conjecture: Let h ε be any natural one-parameter family of asymmetric interface models with ε denoting the strength of the asymmetry such that propagation speed ε. As ε 0, there is a choice of C ε ε 1 such that εhε (ε 1 x, ε 2 t) C ε t converges to solutions h to the KPZ equation. Height function of WASEP (Bertini-Giacomin 97). Accumulation points satisfy weak version of KPZ for certain generalisations of WASEP (Jara-Gonçalves 10).

28 Weak universality result for KPZ Class of models: t h ε = 2 xh ε + εp ( x h ε ) + F, with P an even polynomial, F a Gaussian field with compactly supported correlations ρ(t, x) s.t. ρ = 1. Theorem (H., Quastel 14, in progress) As ε 0, there is a choice of C ε ε 1 such that εh(ε 1 x, ε 2 t) C ε t converges to solutions to (KPZ) λ with λ depending in a non-trivial way on all coefficients of P. Remark: Convergence to KPZ with λ 0 even if P (u) = u 4!!

29 Weak universality result for KPZ Class of models: t h ε = 2 xh ε + εp ( x h ε ) + F, with P an even polynomial, F a Gaussian field with compactly supported correlations ρ(t, x) s.t. ρ = 1. Theorem (H., Quastel 14, in progress) As ε 0, there is a choice of C ε ε 1 such that εh(ε 1 x, ε 2 t) C ε t converges to solutions to (KPZ) λ with λ depending in a non-trivial way on all coefficients of P. Remark: Convergence to KPZ with λ 0 even if P (u) = u 4!!

30 Weak universality result for KPZ Class of models: t h ε = 2 xh ε + εp ( x h ε ) + F, with P an even polynomial, F a Gaussian field with compactly supported correlations ρ(t, x) s.t. ρ = 1. Nonlinearity λ( x h) 2 Theorem (H., Quastel 14, in progress) As ε 0, there is a choice of C ε ε 1 such that εh(ε 1 x, ε 2 t) C ε t converges to solutions to (KPZ) λ with λ depending in a non-trivial way on all coefficients of P. Remark: Convergence to KPZ with λ 0 even if P (u) = u 4!!

31 Weak universality result for KPZ Class of models: t h ε = 2 xh ε + εp ( x h ε ) + F, with P an even polynomial, F a Gaussian field with compactly supported correlations ρ(t, x) s.t. ρ = 1. Theorem (H., Quastel 14, in progress) As ε 0, there is a choice of C ε ε 1 such that εh(ε 1 x, ε 2 t) C ε t converges to solutions to (KPZ) λ with λ depending in a non-trivial way on all coefficients of P. Remark: Convergence to KPZ with λ 0 even if P (u) = u 4!!

32 Weak universality result for KPZ Class of models: t h ε = 2 xh ε + εp ( x h ε ) + F, with P an even polynomial, F a Gaussian field with compactly supported correlations ρ(t, x) s.t. ρ = 1. Theorem (H., Quastel 14, in progress) As ε 0, there is a choice of C ε ε 1 such that εh(ε 1 x, ε 2 t) C ε t converges to solutions to (KPZ) λ with λ depending in a non-trivial way on all coefficients of P. Remark: Convergence to KPZ with λ 0 even if P (u) = u 4!!

33 Case P (u) = u 4 Write h ε (x, t) = εh(ε 1 x, ε 2 t) C ε t. Satisfies t hε = 2 xh ε + ε( x hε ) 4 + ξ ε C ε, with ξ ε an ε-approximation to white noise. Fact: Derivatives of microscopic model do not converge to 0 as ε 0: no small gradients! Heuristic: gradients have O(1) fluctuations but are small on average over large scales... General formula: λ = 1 P (u) µ(du), C ε = 1 P (u) µ(du) + O(1), 2 ε with µ a Gaussian measure, explicitly computable variance.

34 Case P (u) = u 4 Write h ε (x, t) = εh(ε 1 x, ε 2 t) C ε t. Satisfies t hε = 2 xh ε + ε( x hε ) 4 + ξ ε C ε, with ξ ε an ε-approximation to white noise. Fact: Derivatives of microscopic model do not converge to 0 as ε 0: no small gradients! Heuristic: gradients have O(1) fluctuations but are small on average over large scales... General formula: λ = 1 P (u) µ(du), C ε = 1 P (u) µ(du) + O(1), 2 ε with µ a Gaussian measure, explicitly computable variance.

35 Case P (u) = u 4 Write h ε (x, t) = εh(ε 1 x, ε 2 t) C ε t. Satisfies t hε = 2 xh ε + ε( x hε ) 4 + ξ ε C ε, with ξ ε an ε-approximation to white noise. Fact: Derivatives of microscopic model do not converge to 0 as ε 0: no small gradients! Heuristic: gradients have O(1) fluctuations but are small on average over large scales... General formula: λ = 1 P (u) µ(du), C ε = 1 P (u) µ(du) + O(1), 2 ε with µ a Gaussian measure, explicitly computable variance.

36 Main step in proof Rewrite general equation in integral form as H = P ( E(DH) 4 + a(dh) 2 + Ξ ), with E an abstract integration operator of order 1, P convolution with heat kernel. Find two-parameter lift of noise ξ ε Ψ α,c (ξ ε ) so that h = RH solves t h = 2 xh + αh 4 ( x h, c) + ah 2 ( x h, c) + ξ ε = 2 xh + α( x h) 4 + (a 6αc)( x h) 2 + (3αc 2 ac) + ξ ε. Show that Ψ ε,1/ε (ξ ε ) converges to same limit as Ψ 0,1/ε (ξ ε )! (Actually more complicated: logarithmic sub-divergencies...)

37 Main step in proof Rewrite general equation in integral form as H = P ( E(DH) 4 + a(dh) 2 + Ξ ), with E an abstract integration operator of order 1, P convolution with heat kernel. Find two-parameter lift of noise ξ ε Ψ α,c (ξ ε ) so that h = RH solves t h = 2 xh + αh 4 ( x h, c) + ah 2 ( x h, c) + ξ ε = 2 xh + α( x h) 4 + (a 6αc)( x h) 2 + (3αc 2 ac) + ξ ε. Show that Ψ ε,1/ε (ξ ε ) converges to same limit as Ψ 0,1/ε (ξ ε )! (Actually more complicated: logarithmic sub-divergencies...)

38 Main step in proof Rewrite general equation in integral form as H = P ( E(DH) 4 + a(dh) 2 + Ξ ), F M C(S 1 S A ) D γ with E an abstract integration operator of order 1, P convolution withψ heat α,c kernel. R Find two-parameter lift of noise ξ ε Ψ α,c (ξ ε ) so that h = RH F? C(S solves 1 ) C(S 1 R + ) S C t h = xh 2 + αhnoise 4 ( x h, c) + I.C. ah 2 ( x h, c) + ξ ε = xh 2 + α( x h) 4 + (a 6αc)( x h) 2 + (3αc 2 ac) + ξ ε. Show that Ψ ε,1/ε (ξ ε ) converges to same limit as Ψ 0,1/ε (ξ ε )! (Actually more complicated: logarithmic sub-divergencies...)

39 Main step in proof Rewrite general equation in integral form as H = P ( E(DH) 4 + a(dh) 2 + Ξ ), with E an abstract integration operator of order 1, P convolution with heat kernel. Find two-parameter lift of noise ξ ε Ψ α,c (ξ ε ) so that h = RH solves t h = 2 xh + αh 4 ( x h, c) + ah 2 ( x h, c) + ξ ε = 2 xh + α( x h) 4 + (a 6αc)( x h) 2 + (3αc 2 ac) + ξ ε. Show that Ψ ε,1/ε (ξ ε ) converges to same limit as Ψ 0,1/ε (ξ ε )! (Actually more complicated: logarithmic sub-divergencies...)

40 Main step in proof Rewrite general equation in integral form as H = P ( E(DH) 4 + a(dh) 2 + Ξ ), with E an abstract integration operator of order 1, P convolution with heat kernel. Find two-parameter lift of noise ξ ε Ψ α,c (ξ ε ) so that h = RH solves t h = 2 xh + αh 4 ( x h, c) + ah 2 ( x h, c) + ξ ε = 2 xh + α( x h) 4 + (a 6αc)( x h) 2 + (3αc 2 ac) + ξ ε. Show that Ψ ε,1/ε (ξ ε ) converges to same limit as Ψ 0,1/ε (ξ ε )! (Actually more complicated: logarithmic sub-divergencies...)

41 Outlook Many open questions remain: 1. Strong Universality without exact solvability??? 2. Hyperbolic / dispersive problems?? 3. Obtain convergence results for discrete models (H.-Maas-Weber 12; Mourrat-Weber, in progress). 4. Non-Gaussian noise / fully nonlinear continuum models. 5. Control over larger scales KPZ fixed point. 6. Characterisation of possible renormalisation maps. When does it yield a modified equation in closed form? 7. Systematic way of choosing renormalisation procedure and proving convergence (H.-Quastel; Bruned-H.-Zambotti, in progress).

42 Outlook Many open questions remain: 1. Strong Universality without exact solvability??? 2. Hyperbolic / dispersive problems?? 3. Obtain convergence results for discrete models (H.-Maas-Weber 12; Mourrat-Weber, in progress). 4. Non-Gaussian noise / fully nonlinear continuum models. 5. Control over larger scales KPZ fixed point. 6. Characterisation of possible renormalisation maps. When does it yield a modified equation in closed form? 7. Systematic way of choosing renormalisation procedure and proving convergence (H.-Quastel; Bruned-H.-Zambotti, in progress).

43 Outlook Many open questions remain: 1. Strong Universality without exact solvability??? 2. Hyperbolic / dispersive problems?? 3. Obtain convergence results for discrete models (H.-Maas-Weber 12; Mourrat-Weber, in progress). 4. Non-Gaussian noise / fully nonlinear continuum models. 5. Control over larger scales KPZ fixed point. 6. Characterisation of possible renormalisation maps. When does it yield a modified equation in closed form? 7. Systematic way of choosing renormalisation procedure and proving convergence (H.-Quastel; Bruned-H.-Zambotti, in progress).

44 Outlook Many open questions remain: 1. Strong Universality without exact solvability??? 2. Hyperbolic / dispersive problems?? 3. Obtain convergence results for discrete models (H.-Maas-Weber 12; Mourrat-Weber, in progress). 4. Non-Gaussian noise / fully nonlinear continuum models. 5. Control over larger scales KPZ fixed point. 6. Characterisation of possible renormalisation maps. When does it yield a modified equation in closed form? 7. Systematic way of choosing renormalisation procedure and proving convergence (H.-Quastel; Bruned-H.-Zambotti, in progress).

45 Outlook Many open questions remain: 1. Strong Universality without exact solvability??? 2. Hyperbolic / dispersive problems?? 3. Obtain convergence results for discrete models (H.-Maas-Weber 12; Mourrat-Weber, in progress). 4. Non-Gaussian noise / fully nonlinear continuum models. 5. Control over larger scales KPZ fixed point. 6. Characterisation of possible renormalisation maps. When does it yield a modified equation in closed form? 7. Systematic way of choosing renormalisation procedure and proving convergence (H.-Quastel; Bruned-H.-Zambotti, in progress).

46 Outlook Many open questions remain: 1. Strong Universality without exact solvability??? 2. Hyperbolic / dispersive problems?? 3. Obtain convergence results for discrete models (H.-Maas-Weber 12; Mourrat-Weber, in progress). 4. Non-Gaussian noise / fully nonlinear continuum models. 5. Control over larger scales KPZ fixed point. 6. Characterisation of possible renormalisation maps. When does it yield a modified equation in closed form? 7. Systematic way of choosing renormalisation procedure and proving convergence (H.-Quastel; Bruned-H.-Zambotti, in progress).

47 Outlook Many open questions remain: 1. Strong Universality without exact solvability??? 2. Hyperbolic / dispersive problems?? 3. Obtain convergence results for discrete models (H.-Maas-Weber 12; Mourrat-Weber, in progress). 4. Non-Gaussian noise / fully nonlinear continuum models. 5. Control over larger scales KPZ fixed point. 6. Characterisation of possible renormalisation maps. When does it yield a modified equation in closed form? 7. Systematic way of choosing renormalisation procedure and proving convergence (H.-Quastel; Bruned-H.-Zambotti, in progress).

Weak universality of the KPZ equation

Weak universality of the KPZ equation M. Hairer, joint with J. Quastel University of Warwick Buenos Aires, July 29, 2014 KPZ equation Introduced by Kardar, Parisi and Zhang in 1986. Stochastic partial