Contracting differential equations in weighted Banach spaces
Anand Srinivasan, Jean-Jacques Slotine

TL;DR
This paper generalizes geodesic contraction analysis to infinite-dimensional Banach spaces using weighted semi-inner products, enabling fixed point existence and applications to nonlinear PDEs and control systems.
Contribution
It introduces a novel framework for contraction analysis in weighted Banach spaces, extending finite-dimensional concepts to infinite-dimensional PDEs and control applications.
Findings
Negative contraction rates imply asymptotic norm-contraction.
Contraction in weighted spaces leads to convergence to subspaces, submanifolds, or limit cycles.
Method for constructing weak solutions via vanishing Lipschitz approximations.
Abstract
Geodesic contraction in vector-valued differential equations is readily verified by linearized operators which are uniformly negative-definite in the Riemannian metric. In the infinite-dimensional setting, however, such analysis is generally restricted to norm-contracting systems. We develop a generalization of geodesic contraction rates to Banach spaces using a smoothly-weighted semi-inner product structure on tangent spaces. We show that negative contraction rates in bijectively weighted spaces imply asymptotic norm-contraction, and apply recent results on asymptotic contractions in Banach spaces to establish the existence of fixed points. We show that contraction in surjectively weighted spaces verify non-equilibrium asymptotic properties, such as convergence to finite- and infinite-dimensional subspaces, submanifolds, limit cycles, and phase-locking phenomena. We use contraction…
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
TopicsAdvanced Mathematical Modeling in Engineering · Model Reduction and Neural Networks · Numerical methods for differential equations
