Markov Processes: Characterization and Convergence 09: Brownian stochastic equations: existence and uniquenessTextbook
Brownian equations as models of evolving systems
A Brownian stochastic equation describes a state that changes through a deterministic drift and random fluctuations. Such equations are basic models for diffusions and connect probabilistic sample paths with analytic operators. Chapter 5, Section 3 of Stewart Ethier and Thomas Kurtz's Markov Processes: Characterization and Convergence develops this connection in both directions and then separates three questions: whether a solution exists, whether it is unique when driven by fixed noise, and whether all solutions have the same distribution. The mission records four central results from that section as one coherent formalization target. The principal goal is the fixed-driver existence theorem, Theorem 3.11. Theorems 3.3, 3.6, and 3.10 retain the representation, uniqueness, and weak-existence variants without treating one as a consequence of another.
State space, coefficients, and solutions
Fix a dimension . The state space is the Euclidean space , represented in Lean as EuclideanSpace ℝ (Fin d). A diffusion coefficient
controls the random fluctuations, and a drift coefficient
controls the finite-variation part. Given a -dimensional Brownian motion and initial state , a solution satisfies, componentwise,
The formal statement does not postulate an opaque stochastic-integral operator. The predicate HasBrownianItoIntegral uses bounded predictable dyadic step approximations, integrated squared-error convergence in probability, and convergence in probability of the corresponding stochastic sums. SolvesBrownianSDE requires continuous paths, adaptation to the completed natural past of and , integrability of the drift, and one almost-sure equation holding for every time and coordinate.
A weak solution may choose its probability space, filtration, and Brownian driver. Its Brownian future increments are independent of the entire filtration past, its state process is adapted after completion, and its initial distribution is prescribed. Pathwise uniqueness compares two solutions on the same space with the same filtration and driver. Distribution uniqueness compares solutions that may live on different spaces and asks for equality of their whole coordinate-path laws.
Formalization targets
Theorem 3.3: martingale-problem representation
For locally bounded Borel coefficients, a given continuous solution of the diffusion martingale problem can be lifted to the completed product with an auxiliary Brownian space. On that specific extension there exists a Brownian motion making the lifted original process a weak solution of the stochastic equation. The generator is
This target retains the factor , the complete drift term, possibly singular diffusion matrices, the given process, and the exact completed product filtration.
Theorem 3.6: uniqueness implication
For locally bounded Borel and and any initial probability law , pathwise uniqueness implies uniqueness in distribution. No existence, Lipschitz, ellipticity, or moment assumption is added.
Theorem 3.10: weak existence
When both coefficients are continuous and a constant satisfies
there is a weak solution for every initial probability law. The drift hypothesis is one-sided and does not bound .
Theorem 3.11: strong existence on fixed noise
The main goal assumes locally bounded Borel coefficients, the preceding one-sided growth condition locally in time, and local Lipschitz control on each bounded state ball. For every Brownian motion and every independent square-integrable initial variable already given on a probability space, there exists solving the equation with respect to the completed natural past of and . The quantifier order preserves the fixed driver and original probability space.
Why these results matter
The representation theorem connects the analytic martingale problem with the pathwise stochastic-integral formulation while preserving a given process on a prescribed extension. The uniqueness theorem explains when the apparently stronger same-noise comparison controls the law of arbitrary weak solutions. The weak-existence theorem supplies solutions under continuity and one-sided growth without local Lipschitz assumptions. The strong-existence theorem supplies a solution driven by noise and initial data that are fixed in advance, under local Lipschitz regularity.
Formalizing the group creates reusable definitions for completed filtrations, Brownian drivers, local Itô integration, weak solutions, pathwise uniqueness, and time-dependent diffusion generators. The source results are classical theorems, and this proposal records their reviewed Lean statements as open proof obligations. It does not claim machine-checked proofs.
Main formalization difficulty
The central difficulty is keeping the probabilistic quantifiers and filtrations exact. Replacing the local stochastic integral by an unconstrained witness would make the equation too weak. Replacing weak existence by existence on a fixed space would make Theorem 3.10 too strong. Conversely, existentially choosing a new driver in Theorem 3.11 would lose its fixed-noise content. The representation theorem also cannot be reduced to equality in law: it must retain the original process through first projection and use the completed product past specified in the source.
Formalization scope
Time is ℝ≥0, states are finite-dimensional real Euclidean spaces, and diffusion matrices use the Frobenius norm. Brownian motion is represented by independent scalar Brownian coordinates with measurable evaluations and continuous paths. Completion adds every subset of an ambient measurable null set. The local Itô relation uses left-endpoint dyadic step functions, interval integrability, and convergence in probability. Equality of path laws is expressed on the coordinate function space; for continuous Euclidean paths this matches the usual continuous-path law.
The main theorem keeps Borel measurability, compact-set local boundedness, one-sided drift growth, squared diffusion growth, local Lipschitz bounds, independence of and , and the second moment of . The weak theorem keeps continuity and an arbitrary initial law. The representation target keeps compactly supported smooth tests and the exact covariance contraction. These conditions rule out vacuous solution predicates and default-valued integrals. Contributions may prove the four theorem statements or establish reusable lemmas for the concrete integral, completion, martingale, and path-law infrastructure.
Selected references
- Stewart N. Ethier and Thomas G. Kurtz, Markov Processes: Characterization and Convergence, Wiley, 1986, Chapter 5, Section 3, Theorems 3.3, 3.6, 3.10, and 3.11. Wiley