Invariant probability measure Ergodic theorems Construction of the correctors

starting from X ǫ = x, where K ǫ is the local time of X ǫ . It is straightforward to check that, if B ∗ is a Brownian motion, the generator associated to the above RSDE coincides with 7 for sufficiently smooth functions. To sum up, with the help of the Girsanov transform, we can compare the law of the process X ε with that of the RSDE 10 associated to L ǫ V . We shall see that most of the necessary estimates to homogenize the process X ε are valid under P ǫ∗ x . We want to make sure that they remain valid under P ǫ x . To that purpose, the probability measure P ǫ x must be dominated by P ǫ∗ x uniformly with respect to ε. From 9, it is readily seen that C = sup ε0 E ǫ∗ x dP ε x dP ε∗ x 2 1 2 +∞ C only depends on T, |a| ∞ and sup ¯ D |∂ x V |. Then the Cauchy-Schwarz inequality yields ∀ε 0, ∀A F T -measurable subset , P ǫ x A ≤ C P ǫ∗ x A 1 2 . 11 In conclusion, we summarize our strategy: first we shall prove that the process X ε possesses an IPM under the modified law P ǫ∗ , then we establish under P ǫ∗ all the necessary estimates to homogenize X ε , and finally we shall deduce that the estimates remain valid under P ǫ thanks to 11. Once that is done, we shall be in position to homogenize 1. To fix the ideas and to see that the class of functions V satisfying 8 9 is not empty, we can choose V to be equal to V x 1 , . . . , x d = Ax 1 + A1 + x 2 2 + · · · + x 2 d 1 2 + c, 12 for some renormalization constant c such that R ¯ D e −2V x d x = 1 and some positive constant A. Notations for measures. In what follows, ¯ P ǫ resp. ¯ P ǫ∗ stands for the averaged or annealed probability measure M R ¯ D P ǫ x ·e −2V x d x resp. M R ¯ D P ǫ∗ x ·e −2V x d x, and ¯ E ǫ resp. ¯ E ǫ∗ for the corresponding expectation. P ∗ D and P ∗ ∂ D respectively denote the probability measure e −2V x d x ⊗ dµ on ¯D × Ω and the finite measure e −2V x d x ⊗ dµ on ∂ D × Ω. M ∗ D and M ∗ ∂ D stand for the respective expectations.

2.2 Invariant probability measure

As explained above, the main advantage of considering the process X ε under the modified law P ǫ∗ x is that we can find an IPM. More precisely Lemma 2.1. The process X ǫ satisfies: 1For each function f ∈ L 1 ¯ D × Ω; P ∗ D and t ≥ 0: ¯ E ǫ∗ [ f X ǫ t , τ X ǫ t ǫ ω] = M ∗ D [ f ]. 13 2 For each function f ∈ L 1 ∂ D × Ω; P ∗ ∂ D and t ≥ 0: ¯ E ǫ∗ Z t f X ǫ r , τ X ǫ r ǫ ω d K ǫ r = tM ∗ ∂ D f . 14 The first relation 13 results from the structure of L ε V see 7, which has been defined so as to make e −2V x d x invariant for the process X ε . Once 13 established, 14 is derived from the fact that K ε is the density of occupation time of the process X ε at the boundary ∂ D. 995

2.3 Ergodic problems

The next step is to determine the asymptotic behaviour as ε → 0 of the quantities Z t f τ X ε r ε ω d r and Z t f τ X ε r ε ω d K ε r . 15 The behaviour of each above quantity is related to the evolution of the process X ε respectively inside the domain D and near the boundary ∂ D. We shall see that both limits can be identified by solving ergodic problems associated to appropriate resolvent families. What concerns the first functional has already been investigated in the literature. The main novelty of the following section is the boundary ergodic problems associated to the second functional. Ergodic problems associated to the diffusion process inside D First we have to figure out what happens when the process X ε evolves inside the domain D. In that case, the pushing of the local time in 1 vanishes. The process X ε is thus driven by the same effects as in the stationary case without reflection term. The ergodic properties of the process inside D are therefore the same as in the classical situation. So we just sum up the main results and give references for further details. Notations: For p ∈ [1; ∞], L p Ω denotes the standard space of p-th power integrable functions essentially bounded functions if p = ∞ on Ω, G , µ and | · | p the corresponding norm. If p = 2, the associated inner product is denoted by · , · 2 . The space C ∞ c ¯ D resp. C ∞ c D denotes the space of smooth functions on ¯ D with compact support in ¯ D resp. D. Standard background: The operators on L 2 Ω defined by T x g ω = g τ x ω form a strongly contin- uous group of unitary maps in L 2 Ω. Let e 1 , . . . , e d stand for the canonical basis of R d . The group T x x possesses d generators defined by D i g = lim h ∈R→0 h −1 T he i g − g , for i = 1, . . . , d, when- ever the limit exists in the L 2 Ω-sense. The operators D i i are closed and densely defined. Given ϕ ∈ T d i=1 DomD i , Dϕ stands for the d-dimensional vector whose entries are D i ϕ for i = 1, . . . , d. We point out that we distinguish D i from the usual differential operator ∂ x i acting on differentiable functions f : R d → R more generally, for k ≥ 2, ∂ k x i1 ...x ik denotes the iterated operator ∂ x i1 . . . ∂ x ik . However, it is straightforward to check that, whenever a function ϕ ∈ DomD possesses differen- tiable trajectories i.e. µ a.s. the mapping x 7→ ϕτ x ω is differentiable in the classical sense, we have D i ϕτ x ω = ∂ x i ϕτ x ω. We denote by C the dense subspace of L 2 Ω defined by C = Span ¦ g ⋆ ϕ; g ∈ L ∞ Ω, ϕ ∈ C ∞ c R d © where g ⋆ ϕω = Z R d g τ x ωϕx d x 16 Basically, C stands for the space of smooth functions on the random medium. We have C ⊂ DomD i and D i g ⋆ ϕ = −g ⋆ ∂ x i ϕ for all 1 ≤ i ≤ d. This quantity is also equal to D i g ⋆ ϕ if g ∈ DomD i . We associate to the operator L ǫ Eq. 4 an unbounded operator acting on C ⊂ L 2 Ω L = 1 2 D i a i j D j · . 17 996 Following [6, Ch. 3, Sect 3.] see also [19, Sect. 4], we can consider its Friedrich extension, still denoted by L, which is a self-adjoint operator on L 2 Ω. The domain H of the corresponding Dirichlet form can be described as the closure of C with respect to the norm kϕk 2 H = |ϕ| 2 2 + |Dϕ| 2 2 . Since L is self-adjoint, it also defines a resolvent family U λ λ0 . For each f ∈ L 2 Ω, the function w λ = U λ f ∈ H ∩ DomL equivalently solves the L 2 Ω-sense equation λw λ − Lw λ = f 18 or the weak formulation equation ∀ϕ ∈ H, λw λ , ϕ 2 + 12 a i j D i w λ , D j ϕ 2 = f , ϕ 2 . 19 Moreover, the resolvent operator U λ satisfies the maximum principle: Lemma 2.2. For any function f ∈ L ∞ Ω, the function U λ f belongs to L ∞ Ω and satisfies |U λ f | ∞ ≤ | f | ∞ λ. The ergodic properties of the operator L are summarized in the following proposition: Proposition 2.3. Given f ∈ L 2 Ω, the solution w λ of the resolvent equation λw λ − Lw λ = f λ 0 satisfies |λw λ − M[ f ]| 2 → 0 as λ → 0, and ∀λ 0, |λ 1 2 Dw λ | 2 ≤ Λ −12 | f | 2 . Boundary ergodic problems Second, we have to figure out what happens when the process hits the boundary ∂ D. If we want to adapt the arguments in [22], it seems natural to look at the unbounded operator in random medium H γ , whose construction is formally the following: given ω ∈ Ω and a smooth function ϕ ∈ C , let us denote by ˜ u ω : ¯ D → R the solution of the problem ¨ L ω ˜ u ω x = 0, x ∈ D, ˜ u ω x = ϕτ x ω, x ∈ ∂ D. 20 where the operator L ω is defined by L ω f x = 1 2a i j τ x ω∂ 2 x i x j f x + b i τ x ω∂ x i f x 21 whenever f : ¯ D → R is smooth enough, say f ∈ C 2 ¯ D. Then we define H γ ϕω = γ i ω∂ x i ˜ u ω 0. 22 Remark. Choose ε = 1 in 1 and denote by X 1 , K 1 the solution of 1. The operator H γ is actually the generator of the Ω-valued Markov process Z t ω = τ Y t ω ω, where Y t ω = X 1 K −1 t and the function K −1 stands for the left inverse of K 1 : K −1 t = inf{s 0; K 1 s ≥ t}. The process Z describes the environment as seen from the particle whenever the process X 1 hits the boundary ∂ D. 997 The main difficulty lies in constructing a unique solution of Problem 20 with suitable growth and integrability properties because of the lack of compactness of D. This point together with the lack of IPM are known as the major difficulties in homogenizing the Neumann problem in random media. We detail below the construction of H γ through its resolvent family. In spite of its technical aspect, this construction seems to be the right one because it exhibits a lack of stationarity along the e 1 - direction, which is intrinsic to the problem due to the pushing of the local time K ε , and conserves the stationarity of the problem along all other directions. First we give a few notations before tackling the construction of H γ . In what follows, the notation x 1 , y stands for a d-dimensional vector, where the first component x 1 belongs to R eventually R + = [0; +∞ and the second component y belongs to R d −1 . To define an unbounded operator, we first need to determine the space that it acts on. As explained above, that space must exhibit a a lack of stationarity along the e 1 -direction and stationarity along all other directions. So the natural space to look at is the product space R + × Ω, denoted by Ω + , equipped with the measure d µ + d e f = d x 1 ⊗ dµ where d x 1 is the Lebesgue measure on R + . We can then consider the standard spaces L p Ω + for p ∈ [1; +∞]. Our strategy is to define the Dirichlet form associated to H γ . To that purpose, we need to define a dense space of test functions on Ω + and a symmetric bilinear form acting on the test functions. It is natural to define the space of test functions by C Ω + = Span{ρx 1 ϕω; ρ ∈ C ∞ c [0; +∞, ϕ ∈ C }. Among the test functions we distinguish those that are vanishing on the boundary {0} × Ω of Ω + C c Ω + = Span{ρx 1 ϕω; ρ ∈ C ∞ c 0; +∞, ϕ ∈ C }. Before tackling the construction of the symmetric bilinear form, we also need to introduce some elementary tools of differential calculus on Ω + . For any g ∈ CΩ + , we introduce a sort of gradient ∂ g of g . If g ∈ CΩ + takes on the form ρx 1 ϕω for some ρ ∈ C ∞ c [0; +∞ and ϕ ∈ C , the entries of ∂ g are given by ∂ 1 g x 1 , ω = ∂ x 1 g x 1 ϕω, and, for i = 2, . . . , d, ∂ i g x 1 , ω = ρx 1 D i ϕω. We define on CΩ + the norm N g 2 = |g 0, ·| 2 2 + Z Ω + |∂ g | 2 2 d µ + , 23 which is a sort of Sobolev norm on Ω + , and W 1 as the closure of CΩ + with respect to the norm N W 1 is thus an analog of Sobolev spaces on Ω + . Obviously, the mapping P : W 1 ∋ g 7→ g 0, · ∈ L 2 Ω is continuous with norm equal to 1 and stands, in a way, for the trace operator on Ω + . Equip the topological dual space W 1 ′ of W 1 with the dual norm N ′ . The adjoint P ∗ of P is given by P ∗ : ϕ ∈ L 2 Ω 7→ P ∗ ϕ ∈ W 1 ′ where the mapping P ∗ ϕ exactly matches P ∗ ϕ : g ∈ W 1 7→ g , P ∗ ϕ = ϕ, g 0, · 2 . 998 To sum up, we have constructed a space of test functions CΩ + , which is dense in W 1 for the norm N , and a trace operator on W 1 . We further stress that a function g ∈ W 1 satisfies ∂ g = 0 if and only if we have g x 1 , ω = f ω on Ω + for some function f ∈ L 2 Ω invariant under the translations {τ x ; x ∈ {0} × R d −1 }. For that reason, we introduce the σ-field G ∗ ⊂ G generated by the subsets of Ω that are invariant under the translations {τ x ; x ∈ {0} × R d −1 }, and the conditional expectation M 1 with respect to G ∗ . We now focus on the construction of the symmetric bilinear form and the resolvent family associated to H γ . For each random function ϕ defined on Ω, we associate a function ϕ + defined on Ω + by ∀x 1 , ω ∈ Ω + , ϕ + x 1 , ω = ϕτ x 1 ω. Hence, we can associate to the random matrix a defined in Section 1 the corresponding matrix- valued function a + defined on Ω + . Then, for any λ 0, we define on W 1 × W 1 the following symmetric bilinear form B λ g , h = λP g , P h 2 + 1 2 Z Ω + a + i j ∂ i g ∂ j h d µ + . 24 From 5, it is readily seen that it is continuous and coercive on W 1 × W 1 . From the Lax-Milgram theorem, it thus defines a continuous resolvent family G λ : W 1 ′ → W 1 such that: ∀F ∈ W 1 ′ , ∀g ∈ W 1 , B λ G λ

F, g = g , F . 25

For each λ 0, we then define the operator R λ : L 2 Ω → L 2 Ω ϕ 7→ PG λ P ∗ ϕ . 26 Given ϕ ∈ L 2 Ω, we can plug F = P ∗ ϕ into 25 and we get ∀g ∈ W 1 , B λ G λ P ∗ ϕ, g = g , P ∗ ϕ, 27 that is, by using 24: ∀g ∈ W 1 , λR λ ϕ, Pg 2 + 1 2 Z Ω + a + i j ∂ i G λ P ∗ ϕ ∂ j g d µ + = g 0, ·, ϕ 2 , 28 The following proposition summarizes the main properties of the operators R λ λ0 , and in partic- ular their ergodic properties: Proposition 2.4. The family R λ λ is a strongly continuous resolvent family, and: 1 the operator R λ is self-adjoint. 2 given ϕ ∈ L 2 Ω and λ 0, we have: ϕ ∈ KerλR λ − I ⇔ ϕ = M 1 [ϕ]. 3 for each function ϕ ∈ L 2 Ω, |λR λ ϕ − M 1 [ϕ]| 2 → 0 as λ → 0. The remaining part of this section is concerned with the regularity properties of G λ P ∗ ϕ. 999 Proposition 2.5. Given ϕ ∈ C , the trajectories of G λ P ∗ ϕ are smooth. More precisely, we can find N ⊂ Ω satisfying µN = 0 and such that ∀ω ∈ Ω \ N, the function ˜ u ω : x = x 1 , y ∈ ¯D 7→ G λ P ∗ ϕx 1 , τ 0, y ω belongs to C ∞ ¯ D. Furthermore, it is a classical solution to the problem: ¨ L ω ˜ u ω x = 0, x ∈ D, λ˜ u ω x − γ i τ x ω∂ x i ˜ u ω x = ϕτ x ω, x ∈ ∂ D. 29 In particular, the above proposition proves that R λ λ is the resolvent family associated to the oper- ator H γ . This family also satisfies the maximum principle: Proposition 2.6. Maximum principle. Given ϕ ∈ C and λ 0, we have: |G λ P ∗ ϕ | L ∞ Ω + ≤ λ −1 |ϕ| ∞ .

2.4 Ergodic theorems

As already explained, the ergodic problems that we have solved in the previous section lead to establishing ergodic theorems for the process X ε . The strategy of the proof is the following. First we work under ¯ P ǫ∗ to use the existence of the IPM see Section 2.2. By adapting a classical scheme, we derive from Propositions 2.3 and 2.4 ergodic theorems under ¯ P ǫ∗ both for the process X ε and for the local time K ε : Theorem 2.7. For each function f ∈ L 1 Ω and T 0, we have lim ǫ→0 ¯ E ǫ∗ h sup ≤t≤T Z t f τ X ǫ r ǫ ω d r − tM[ f ] i = 0. 30 Theorem 2.8. If f ∈ L 2 Ω, the following convergence holds lim ǫ→0 ¯ E ǫ∗ h sup ≤t≤T Z t f τ X ǫ r ǫ ω d K ǫ r − M 1 [ f ]ωK ǫ t i = 0. 31 Finally we deduce that the above theorems remain valid under ¯ P ǫ thanks to 11. Theorem 2.9. 1 Let f ǫ ǫ be a family converging towards f in L 1 Ω. For each fixed δ 0 and T 0, the following convergence holds lim ǫ→0 ¯ P ǫ h sup ≤t≤T | Z t f ǫ τ X ǫ r ǫ ω d r − tM[ f ]| ≥ δ i = 0. 32 2 Let f ǫ ǫ be a family converging towards f in L 2 Ω. For each fixed δ 0 and T 0, the following convergence holds lim ǫ→0 ¯ P ǫ h sup ≤t≤T Z t f ǫ τ X ǫ r ǫ ω d K ǫ r − M 1 [ f ]K ǫ t ≥ δ i = 0. 33 1000

2.5 Construction of the correctors

Even though we have established ergodic theorems, this is not enough to find the limit of equation 1 because of the highly oscillating term ǫ −1 b τ X ǫ t ǫ ω d t. To get rid of this term, the ideal situation is to find a stationary solution u i : Ω → R to the equation − Lu i = b i . 34 Then, by applying the Itô formula to the function u i , it is readily seen that the contribution of the term ǫ −1 b i τ X ǫ t ǫ ω d t formally reduces to a stochastic integral and a functional of the local time, the limits of which can be handled with the ergodic theorems 2.9. Due to the lack of compactness of a random medium, finding a stationary solution to 34 is rather tricky. As already suggested in the literature, a good approach is to add some coercivity to the problem 34 and define, for i = 1, . . . , d and λ 0, the solution u i λ of the resolvent equation λu i λ − Lu i λ = b i . 35 If we let λ go to 0 in of 35, the solution u i λ should provide a good approximation of the solution of 34. Actually, it is hopeless to prove the convergence of the family u i λ λ in some L p Ω-space because, in great generality, there is no stationary L p Ω-solution to 34. However we can prove the convergence towards 0 of the term λu i λ and the convergence of the gradients Du i λ : Proposition 2.10. There exists ζ i ∈ L 2 Ω d such that λ|u i λ | 2 2 + |Du i λ − ζ i | 2 → 0, as λ → 0. 36 As we shall see in Section 2.6, the above convergence is enough to carry out the homogenization procedure. The functions ζ i i ≤ d are involved in the expression of the coefficients of the ho- mogenized equation 6. For that reason, we give some further qualitative description of these coefficients: Proposition 2.11. Define the random matrix-valued function ζ ∈ L 2 Ω; R d ×d by its entries ζ i j = ζ j i = lim λ→0 D i u j λ . Define the matrix ¯ A and the d-dimensional vector ¯ Γ by ¯ A = M[I + ζ ∗ aI + ζ], which also matches M[I + ζ ∗ a], 37 ¯ Γ = M[I + ζ ∗ γ] ∈ R d , 38 where I denotes the d-dimensional identity matrix. Then ¯ A obeys the variational formula: ∀X ∈ R d , X ∗ ¯ AX = inf ϕ ∈C M [X + Dϕ ∗ aX + D ϕ]. 39 Moreover, we have ¯ A ≥ ΛI in the sense of symmetric nonnegative matrices and the first component ¯Γ 1 of ¯ Γ satisfies ¯ Γ 1 ≥ Λ. Finally, ¯Γ coincides with the orthogonal projection M 1 [I + ζ ∗ γ]. In particular, we have established that the limiting equation 6 is not degenerate, namely that the diffusion coefficient ¯ A is invertible and that the pushing of the reflection term ¯ Γ along the normal to ∂ D does not vanish. 1001

2.6 Homogenization