Reconstruction of the connection or metric from some partial information
J. Mike\v{s}, A. Van\v{z}urov\'a

TL;DR
This paper presents methods to reconstruct the metric tensor and symmetric linear connection of a (pseudo-)Riemannian manifold from partial initial data and curvature components, using differential equations and semigeodesic coordinates.
Contribution
It introduces a generalized approach for reconstructing metrics and connections from partial information, extending previous methods with shorter, more direct proofs.
Findings
Reconstruction of the metric tensor from boundary data and curvature components.
Reconstruction of the symmetric linear connection from initial conditions and curvature.
Shorter, constructive proofs based on classical ODE theory.
Abstract
In a neighborhood of a (positive definite) Riemannian space in which special, semigeodesic, coordinates are given, the metric tensor can be calculated from its values on a suitable hypersurface and some of components of the curvature tensor of type in the coordinate domain. Semigeodesic coordinates are a generalization of the well-known Fermi coordinates, that play an important role in mechanics and physics, are widely used in Minkowskian space, and in differential geometry of Riemannian spaces in general. In the present paper, we consider a more general situation. We introduce special pre-semigeodesic charts characterized both geometrically and in terms of the connection, formulate a version of the Peano's-Picard's-Cauchy-like Theorem on existence and uniqueness of solutions of the initial values problems for systems of first-order ordinary differential equations. Then we use…
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 · Advanced Numerical Analysis Techniques · Topological and Geometric Data Analysis
