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This work aims to advance model order reduction techniques for time-dependent parametrized variational inequalities in crowd motion scenarios.

May 10, 2026Open Access

Model order reduction for parametrized variational inequalities: application to crowd motion

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Randomized trial investigates model order reduction techniques for crowd motion, indicating significant improvements in computational efficiency.

This work aims to advance model order reduction techniques for time-dependent parametrized variational inequalities in crowd motion scenarios.
Investigated a greedy index selection algorithm for compressing Lagrange multipliers.
Applied Proper Orthogonal Decomposition (POD) to velocity snapshots.
Compared the Empirical Interpolation Method (EIM) and the Empirical Quadrature (EQ) procedure.
Significant variability in agent positions and contact forces was observed due to geometric parameter variations.
The nonlinear approach showed improved accuracy over traditional models in a complex environment with many agents.
The methodology demonstrated its efficacy in highly congested scenarios, paving the way for further developments.

Sambataro et al. (2026) studied this question.

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Randomized trial investigates model order reduction techniques for crowd motion, indicating significant improvements in computational efficiency.