Partially Resolved

Higher-dimensional change-set (interface) estimation for discontinuous diffusivity

Sourced from the work of Markus Reiß, Claudia Strauch, Lukas Trottner

§ Problem Statement

Setup

Let d2d\ge 2, let DRdD\subset\mathbb R^d be a bounded domain with C2C^2 boundary, fix T>0T>0, and fix known constants ϑ1,ϑ2\vartheta_1,\vartheta_2 with 0<ϑ1ϑ2<0<\vartheta_1\neq \vartheta_2<\infty. For an unknown measurable set GDG\subset D, define the diffusivity

ϑG(x)=ϑ11G(x)+ϑ21DG(x),xD.\vartheta_G(x)=\vartheta_1\mathbf 1_G(x)+\vartheta_2\mathbf 1_{D\setminus G}(x),\qquad x\in D.

Consider the stochastic parabolic equation (in weak/mild sense)

dut(x)= ⁣ ⁣(ϑG(x)ut(x))dt+BdWt(x),(t,x)(0,T]×D,du_t(x)=\nabla\!\cdot\!\big(\vartheta_G(x)\nabla u_t(x)\big)\,dt + B\,dW_t(x),\qquad (t,x)\in(0,T]\times D,

with boundary condition ut(x)=0u_t(x)=0 for xDx\in\partial D, initial condition u0L2(D)u_0\in L^2(D) known, WW a cylindrical Wiener process on L2(D)L^2(D), and BB a known linear noise operator chosen so the equation is well posed.

This setup follows Reiß et al. (2025).

For each spatial resolution δ>0\delta>0, assume one observes the locally averaged field

Yδ(t,x)=DKδ(xz)ut(z)dz,(t,x)[0,T]×Dδ,Y_\delta(t,x)=\int_D K_\delta(x-z)u_t(z)\,dz,\qquad (t,x)\in[0,T]\times D_\delta,

where Kδ(z)=δdK(z/δ)K_\delta(z)=\delta^{-d}K(z/\delta) for a known compactly supported kernel KK with RdK=1\int_{\mathbb R^d}K=1, and Dδ={xD:dist(x,D)>δrad(suppK)}D_\delta=\{x\in D:\operatorname{dist}(x,\partial D)>\delta\,\operatorname{rad}(\operatorname{supp}K)\}. Thus δ0\delta\to 0 corresponds to increasingly local spatial measurements over fixed time horizon [0,T][0,T].

Unsolved Problem

Assume GG belongs to a geometric class G\mathcal G (for example, sets whose interface G\partial G is a compact embedded CβC^\beta hypersurface with uniformly bounded curvature/reach and dist(G,D)r0>0\operatorname{dist}(\partial G,\partial D)\ge r_0>0). The problem is to construct estimators G^δ=G^δ(Yδ)\widehat G_\delta=\widehat G_\delta(Y_\delta) (equivalently G^δ\widehat{\partial G}_\delta) and determine asymptotic inference limits as δ0\delta\to 0, including:

§ Discussion

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§ Significance & Implications

The one-dimensional unknown jump location is the simplest instance of a geometric inverse problem. Extending to unknown interfaces broadens applicability and connects SPDE inference with nonparametric boundary estimation in higher dimensions. See Reiß, Strauch, and Trottner (Annals of Statistics, 2025) and subsequent multivariate follow-up work.

§ Known Partial Results

  • Reiß et al. (2025): Reiß-Strauch-Trottner (Annals of Statistics, 2025) provide the full technical treatment for the 1D single-discontinuity setting. A multivariate follow-up (arXiv:2504.18023; SPA, 2026) gives additional higher-dimensional/interface results under specific assumptions. A fully sharp, fully general minimax and limit-theory characterization across broad geometric classes remains only partially resolved.

§ References

[1]

Change Point Estimation for a Stochastic Heat Equation

Markus Reiß, Claudia Strauch, Lukas Trottner (2025)

Annals of Statistics 53(3):1540-1572

📍 Section 4 (Discussion), "Perspectives" paragraph on estimating a higher-dimensional change domain/interface.

Published source paper; preprint available at https://arxiv.org/abs/2307.10960.

[2]

Multivariate follow-up on change-set/interface estimation for stochastic heat equations

Unknown (2026)

Stochastic Processes and their Applications (2026)

Post-2023 progress with multivariate/interface results; cited for scoped status update.

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