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Martin Hairer is a mathematician whose work joins probability theory and analysis. He developed regularity structures to give precise meaning to classes of singular stochastic partial differential equations. His EPFL biography identifies this work as central to his 2014 Fields Medal.^[ref]

The motivating obstacle is that a noisy solution can be too irregular for the nonlinear operations appearing in its own equation. Smoothing the input makes an ordinary equation possible, but the resulting solutions need not converge when the smoothing is removed. Regularity structures replace ordinary polynomial local expansions with expansions tailored to the singular input and organize the necessary corrections. The theory proves convergence of appropriate corrected approximations for the equations it covers.^[ref]

His An Introduction to Stochastic PDEs by Martin Hairer gives a complementary starting point through the stochastic heat equation and mild solutions. Integrating the heat evolution against a random driving term clarifies why solving a noisy equation requires specifying both the evolution operator and the interpretation of the noise.^[ref]

A model of a regularity structure realizes the abstract symbols of a regularity structure $(A,T,G)$ as concrete distributions, consistently across expansion points. It comprises continuous linear realization maps ${\Pi }_x:T\to {\mathcal{S}}^{\prime }({\mathbb{R}}^d)$ and maps ${\Gamma }_{xy}\in G$ satisfyingThus ${\Gamma }_{xy}$ transports an expansion from $y$ to $x$ without changing the represented distribution.^[ref]

Analytic bounds connect each symbol's assigned order $\alpha$ to its actual small-scale size. For ordinary isotropic scaling, put ${\phi }_x^{\lambda }(z)={\lambda }^{-d}\phi ((z-x)/\lambda )$. For $\tau \in T_{\alpha }$ one requiresThese bounds are uniform on each compact set of base points, below each fixed upper order, for $0<\lambda \le 1$ and smooth test functions supported in the unit ball with $C^r$ norm at most one; choose an integer $r>\max (0,-\inf A)$. Other scaling choices change the rescaled test functions and distance. Negative $\alpha$ allows singular distributions, whose small-scale averages can grow as $\lambda$ shrinks.^[ref]

In the one-dimensional polynomial model of a regularity structure, $({\Pi }_x{X}^n)(z)=(z-x{)}^n$ and ${\Gamma }_{xy}X=X+x-y$. For $x=1,y=2$,A change of variable $z=x+\lambda w$ in the test function pairing gives exactly ${\lambda }^n\int w^n\phi (w)dw$, explaining the analytic bound for the monomial $X^n$. The realization maps supply information absent from the abstract triple alone.

The polynomial model of a regularity structure realizes abstract monomials as ordinary coordinate differences around each base point. It is the example that turns a regularity structure into familiar Taylor polynomials. In $d$ dimensions, use a polynomial ring in symbols $X_1,\dots ,X_d$, graded by total degree. For a multi-index $k=(k_1,\dots ,k_d)$,Re-expansion uses ${\Gamma }_{xy}p(X)=p(X+x-y)$. Substitution immediately gives ${\Pi }_x{\Gamma }_{xy}={\Pi }_y$ and ${\Gamma }_{xy}{\Gamma }_{yz}={\Gamma }_{xz}$, the compatibility identities of a model of a regularity structure.^[ref]

For example, $f(z_1,z_2)=z_1{z}_2$ has local abstract expansion $a_x=2+2X_1+X_2+X_1{X}_2$ at $x=(1,2)$. At $y=(3,4)$ it has $a_y=12+4X_1+3X_2+X_1{X}_2$. Transporting the latter substitutes $X_1-2,X_2-2$:The coefficients change because the expansion point changes; the represented function is still $z_1{z}_2$.

The analytic scaling also follows by calculation. For a smooth test function and ${\phi }_x^{\lambda }(z)={\lambda }^{-d}\phi ((z-x)/\lambda )$, changing variables givesThus the assigned degree predicts exactly the power of $\lambda$ in the model bound. Anisotropic scaling can assign different weights to different coordinates instead; ordinary total degree is the isotropic choice. General regularity structures retain this organization of local expansions while admitting symbols that are not polynomials.

A regularity structure is an algebraic framework for local expansions that can include singular distributions as well as smooth functions. Its formal data are $(A,T,G)$: a locally finite set $A\subset \mathbb{R}$ containing zero and bounded below; a graded vector space $T={⨁}_{\alpha \in A}{T}_{\alpha }$ of Banach spaces, with $T_0$ the constants; and a group $G$ of linear maps fixing $1$ such thatThus changing an expansion's base point modifies a symbol only through lower-homogeneity terms. Martin Hairer introduced this framework to treat classes of singular stochastic partial differential equations.^[ref]

Ordinary polynomials provide a complete elementary example. In one dimension take $A=\{0,1,2,\dots \}$, $T_n$ spanned by $X^n$, and realize $X^n$ around $x$ as $(z-x{)}^n$. The function $f(z)=z^2$ then has abstract local expansion $a_x=x^2+2xX+X^2$. To transfer the expansion from $y$ to $x$, substitute $X\mapsto X+x-y$. For example,The expression on the left is the expansion at $y=2$ transported to $x=1$. In particular, ${\Gamma }_{12}{X}^2-X^2=-2X+1$ has strictly lower degree, illustrating the defining triangular property.

A model of a regularity structure adds concrete distributions and analytic bounds to these abstract symbols. Writing their realizations as ${\Pi }_x$, compatibility requires ${\Pi }_x{\Gamma }_{xy}={\Pi }_y$ and ${\Gamma }_{xy}{\Gamma }_{yz}={\Gamma }_{xz}$. For singular equations the symbols extend beyond monomials; constructing a model, proving reconstruction and controlling corrections are additional steps. The triple alone does not make arbitrary products of distributions meaningful.^[ref]