An Implicit Function Method for Computing the Stability Boundaries of Hill's Equation
Karthik Chikmagalur, Bassam Bamieh

TL;DR
This paper introduces a novel, more accurate numerical method for computing the stability boundaries of Hill's equation, applicable to both damped and undamped systems, improving upon traditional matrix trace criteria.
Contribution
The paper presents an implicit function method that enhances accuracy and efficiency in determining stability regions of Hill's equation, including damped cases, extending existing techniques.
Findings
The new method improves computational efficiency over traditional approaches.
It provides a more accurate determination of stability boundaries.
The approach generalizes to damped Hill's equations, broadening its applicability.
Abstract
Hill's equation is a common model of a time-periodic system that can undergo parametric resonance for certain choices of system parameters. For most kinds of parametric forcing, stable regions in its two-dimensional parameter space need to be identified numerically, typically by applying a matrix trace criterion. By integrating ODEs derived from the stability criterion, we present an alternative, more accurate and computationally efficient numerical method for determining the stability boundaries of Hill's equation in parameter space. This method works similarly to determine stability boundaries for the closely related problem of vibrational stabilization of the linearized Katpiza pendulum. Additionally, we derive a stability criterion for the damped Hill's equation in terms of a matrix trace criterion on an equivalent undamped system. In doing so we generalize the method of this paper…
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
TopicsModel Reduction and Neural Networks · Numerical methods for differential equations
