On the stability of POD Basis Interpolation via Grassmann Manifolds for Parametric Model Order Reduction in Hyperelasticity
Orestis Friderikos (LMT), Emmanuel Baranger (LMT), Marc Olive (LMT),, David N\'eron (LMT)

TL;DR
This paper investigates the stability of POD basis interpolation on Grassmann manifolds for parametric model order reduction in hyperelasticity, providing explicit stability conditions and analyzing their practical implications.
Contribution
It introduces new stability conditions for POD basis interpolation on Grassmann manifolds, linking geometric properties to stability in hyperelasticity applications.
Findings
Stability can be lost if geometric conditions are not met.
Explicit stability conditions are derived from Grassmannian exponential map properties.
Numerical examples confirm the relevance of the stability conditions.
Abstract
This work considers the stability of Proper Orthogonal Decomposition (POD) basis interpolation on Grassmann manifolds for parametric Model Order Reduction (pMOR) in hyperelasticity. The article contribution is mainly about stability conditions, all defined from strong mathematical background. We show how the stability of interpolation can be lost if certain geometrical requirements are not satisfied by making a concrete elucidation of the local character of linearization. To this effect, we draw special attention to the Grassmannian Exponential map and optimal injectivity condition of this map, related to the cut--locus of Grassmann manifolds. From this, explicit stability conditions are established and can be directly used to determine the loss of injectivity in practical pMOR applications. Another stability condition is formulated when increasing the number p of mode, deduced from…
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Taxonomy
TopicsModel Reduction and Neural Networks · Elasticity and Material Modeling · Vehicle Dynamics and Control Systems
