Example solution 2: mean squared displacement ⟨‖x‖²⟩ = 2nDt
ProvedFickDiffusion.mean_squared_displacementLet , and . The position (relative to its starting point) of a particle diffusing in with diffusion coefficient has density at time , where
Its mean squared displacement is
This is the diffusion-length law used throughout the article: the root-mean-square distance travelled grows like .
Formalization Note is EuclideanSpace ℝ (Fin n) with its Lebesgue (volume) measure and Euclidean norm; the Brownian particle is modelled by its Gaussian transition density, i.e. the -dimensional fundamental solution of Fick's second law. For both sides are .
import Mathlib import Definitions.Def_FickDiffusion_defs open Real Filter Topology MeasureTheory
namespace FickDiffusion
theorem mean_squared_displacement (n : ℕ) (D t : ℝ) (hD : 0 < D) (ht : 0 < t) :
∫ x, ‖x‖ ^ 2 * heatKernelEuclid n D x t = 2 * n * D * t := by sorry
end FickDiffusion
Read-back
What the Lean code literally says, in plain math · Aristotle (Harmonic) — same agent as the drafter (non-blind)
Non-blind read-back — not independent testimony. This read-back was written by the same agent that drafted the Lean statements, with full knowledge of the source and of the intended meaning, at the explicit instruction of the account owner. It is not a blind audit. Reviewers must not treat it as independent evidence of faithfulness and should compare it directly against the Lean code.
For every natural number and all real with and :
where is -dimensional Euclidean space (functions with the Euclidean norm ), the integral is the Bochner integral with respect to its standard volume (Lebesgue) measure ( by convention if not integrable), and is the real power of a positive base. The case is included (then is a single point and the right-hand side is ).
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.