AI & ML
Sampling Design and Confidence Tubes for Integral Curves on Riemannian Manifolds
arXiv stat.ML0 views
arXiv:2611.00771v2 Announce Type: replace Abstract: We estimate the integral curve of an unknown vector field from noisy tangent vector field on a known compact Riemannian manifold. We consider two sampling settings: independent observations at random locations that have already been collected, and a fixed response budget that allows subsequent locations to be chosen using pilot information. Under suitable regularity assumptions, the passive kernel estimator has curve error of order $n^{-2/(d+3)}$ in probability, with sample size $n$ and manifold dimension $d$. Its functional central limit theorem has a limit characterized by a linear stochastic differential equation. For active sampling, an independent pilot directs one new batch of measurements to neighborhoods of the target curve. Under localization, smoothness and design conditions, this concentration yields expected maximum curve error of order $N^{-1/2}$, including the pilot in the total response budget $N$. Both methods provide simultaneous geodesic confidence tubes with asymptotically exact coverage under mild conditions. Simulations show narrower active tubes in most reported settings, with improved small-sample coverage in some three-dimensional examples. Ocean-drifter observations illustrate mean-flow reconstruction on the sphere, and a canopy-flow LDA application uses retrospective active sampling to quantify lateral and vertical departures of local mean streamlines.
Source:https://arxiv.org/abs/2611.00771PreviousInference and Uncertainty Quantification for Streaming $r$-PCANextDistributed Geometric Optimization and Control of Rigid Differential-Drive Formations from Relative-Position Measurements
Related posts

Amazon will let you text Alexa your shopping list
0The Verge

TikTok’s For You page needs to change, says the EU
0The Verge

Stranger Things is getting a live-action spinoff called Other Worlds
0The Verge
The Cost of Self-Hosting Open-Source LLMs: GPUs, Staffing, and Operations
0dev.to
The central challenge of working with AI
0dev.to
Where an e-commerce explainer video belongs, and what to show in it
0dev.to