Theorem 1: the marginal continuity equation
ProvedFlowMatchingT1.theorem_oneLet , , a Borel probability measure on , a real conditional density, and an -valued conditional velocity. At every , assume strict positivity and joint measurability of , normalization and Lebesgue integrability in for every , and -integrability in for every . At every interior time and position, assume the local time-domination conditions for and local spatial-domination conditions for : integrability at the base point, local almost-everywhere strong measurability, measurable derivatives at that point, and differentiability on a common neighborhood with a derivative-norm bound integrable in .
Suppose that for every and every , the conditional density has time derivative for -almost every . Define , , and . Then is a strictly positive probability density for every , the flux is spatially differentiable for interior times, and
Formalization Note. This target formalizes the continuity-equation formulation of Theorem 1 with a concrete sufficient interpretation of Appendix A's Leibniz-rule assumptions. It allows arbitrary conditioning probability measures, including empirical ones. It does not assert an endpoint time derivative or the separate equivalence between the PDE and transport by a global ODE flow.
import Definitions.Def_FlowMatchingT1 open MeasureTheory open FlowMatchingT1
theorem FlowMatchingT1.theorem_one
{d : ℕ} (Q : Measure (Space d)) [IsProbabilityMeasure Q]
(ρ : ℝ → Space d → Space d → ℝ) (v : ℝ → Space d → Space d → Space d)
(hρ : DensityHypotheses Q ρ) (hreg : AnalyticHypotheses Q ρ v)
(hconditional : ∀ t ∈ Set.Ioo (0 : ℝ) 1, ∀ x, ∀ᵐ z ∂Q,
HasDerivAt (fun s => ρ s x z)
(-divergence (fun y => conditionalFlux ρ v t y z) x) t) :
(∀ t ∈ Set.Icc (0 : ℝ) 1,
ProbabilityDensity (marginalDensity Q ρ t) ∧
∀ x, 0 < marginalDensity Q ρ t x) ∧
ContinuityEquation (marginalDensity Q ρ) (marginalVelocity Q ρ v) := by sorryRead-back
What the Lean code literally says, in plain math · gpt-6-astra
For every natural number , let , with its usual coordinatewise real vector-space structure, product topology, maximum norm, and Lebesgue measure ; let be any probability measure on (so ); and let and be arbitrary functions satisfying the following assumptions. Write for scalar multiplication of the vector by the real number . For every and every , ; for every such , the function is measurable on ; for every such and every , the function is everywhere nonnegative, is integrable with respect to , and satisfies ; and for every such and every , the function is integrable with respect to . For every and every , additionally assume all of the following time regularity conditions: is -integrable; there is a neighborhood of on which, for every real , is almost everywhere strongly measurable with respect to ; the function is almost everywhere strongly measurable with respect to ; and there exist a set containing a neighborhood of and a -integrable real function such that, for -almost every , for every , and, for -almost every , is differentiable at every . For every and every , also assume all of the following spatial regularity conditions: is -integrable; there is a neighborhood of on which, for every , is almost everywhere strongly measurable with respect to ; the function , taking values in the continuous real linear maps from to , is almost everywhere strongly measurable with respect to ; and there exist a set containing a neighborhood of and a -integrable real function such that, for -almost every , for every , and, for -almost every , is Fréchet differentiable at every . The neighborhoods and bounds in these assumptions may depend on and ; in each bound and differentiability assertion, the exceptional null set is independent of the point ranging over the indicated neighborhood, while the separate assertions may initially have different exceptional null sets. Here denotes the real Fréchet derivative of at , with operator norm induced by the maximum norm on ; scalar derivatives and Fréchet derivatives used as functions are assigned the value zero at points where the corresponding function is not differentiable. Let be the vector with coordinate at and elsewhere, and define the divergence of any function at to be , using that same total Fréchet derivative convention. The final assumption is that, for every and every , for -almost every the scalar function has a derivative at equal to ; the exceptional null set in this assumption is allowed to depend on both and . Define, for every real and every , , , and , where the vector integral is a Bochner integral and the reciprocal of zero is defined to be zero. The theorem asserts both that, for every , is everywhere nonnegative, is -integrable, has , and satisfies for every , and that, for every and every , the function is Fréchet differentiable at and the function has a derivative at equal to . Thus the derivative assertion is pointwise for every interior time and every spatial point; there is no derivative assertion at or . The integrals defining , , and are total integrals, returning zero when the corresponding integrand is not integrable; the stated hypotheses ensure integrability of 's integrand throughout and of 's integrand throughout , but do not require the latter at the two endpoints or either integrability condition at other times. Positivity in the conclusion makes the reciprocal in nonzero and ordinary division throughout . No absolute continuity condition on is assumed. The dimension is included: is then a singleton vector space, the divergence is an empty sum and equals zero, and every vector field takes the unique zero-vector value; the derivative asserted in the conclusion is consequently zero. All almost-everywhere statements are with respect to the specified probability measure ; no single common full-measure set for all times and all spatial points is required.
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.