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Main Authors: An, Chenyang, Xu, Xiaoqian
Format: Preprint
Published: 2026
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Online Access:https://arxiv.org/abs/2605.20623
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author An, Chenyang
Xu, Xiaoqian
author_facet An, Chenyang
Xu, Xiaoqian
contents We establish explicit lower bounds for advection-diffusion equations in three settings: a polynomial $\dot H^{-1}$ bound for inviscid shears with $u\in L^\infty_t W^{1,1}_y$, a uniform positive lower bound on the mixing scale for diffusive shears, and an exponential $L^2$ bound for rapidly oscillating time-periodic flows. All constants are explicit in the data. The proofs were generated entirely by a multi-agent math proving system, QED, without expert human intervention, serving as a test of AI's capability to produce rigorous mathematics.
format Preprint
id arxiv_https___arxiv_org_abs_2605_20623
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Lower Bounds for Advection-Diffusion Equations: An Exploration with AI-Generated Proofs
An, Chenyang
Xu, Xiaoqian
Analysis of PDEs
Artificial Intelligence
35Q35
We establish explicit lower bounds for advection-diffusion equations in three settings: a polynomial $\dot H^{-1}$ bound for inviscid shears with $u\in L^\infty_t W^{1,1}_y$, a uniform positive lower bound on the mixing scale for diffusive shears, and an exponential $L^2$ bound for rapidly oscillating time-periodic flows. All constants are explicit in the data. The proofs were generated entirely by a multi-agent math proving system, QED, without expert human intervention, serving as a test of AI's capability to produce rigorous mathematics.
title Lower Bounds for Advection-Diffusion Equations: An Exploration with AI-Generated Proofs
topic Analysis of PDEs
Artificial Intelligence
35Q35
url https://arxiv.org/abs/2605.20623