Structure of Metric $1$-currents: approximation by normal currents and representation results
David Bate, Emanuele Caputo, Jakub Tak\'a\v{c}, Phoebe Valentine, Pietro Wald

TL;DR
This paper proves the 1-dimensional flat chain conjecture in metric spaces, showing metric 1-currents can be approximated by normal currents and represented as superpositions of rectifiable sets, extending previous results.
Contribution
It introduces a new Banach space isomorphism theorem linking metric 1-currents to the Arens-Eells space and generalizes representation results to all Banach spaces.
Findings
Metric 1-currents can be approximated in mass by normal 1-currents.
Any metric 1-current can be represented as an integral superposition of rectifiable sets.
Established a strict polyhedral approximation theorem for Banach spaces.
Abstract
We prove the -dimensional flat chain conjecture in any complete and quasiconvex metric space, namely that metric -currents can be approximated in mass by normal -currents. The proof relies on a new Banach space isomorphism theorem, relating metric -currents and their boundaries to the Arens-Eells space. As a by-product, any metric -current in a complete and separable metric space can be represented as the integral superposition of oriented -rectifiable sets, thus dropping a finite dimensionality condition from previous results of Schioppa [Schioppa Adv. Math. 2016, Schioppa J. Funct. Anal. 2016]. The connection between the flat chain conjecture and the representation result is provided by a structure theorem for metric -currents in Banach spaces, showing that any such current can be realised as the restriction to a Borel set of a boundaryless normal -current.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGeometric Analysis and Curvature Flows · Point processes and geometric inequalities · Optimization and Variational Analysis
