Abstract
As formal verification techniques for cyber-physical systems encounter large plant models, techniques for simplifying these models into smaller approximate models are gaining increasing popularity. Model-order reduction techniques take large ordinary differential equation models and simplify them to yield models that are potentially much smaller in size. These approaches typically discover a suitable projection of the state space into a smaller subspace, such that by projecting the dynamics in this subspace, an accurate approximation can be obtained for a given initial set and time horizon of interest. In this paper, we present a study of model-order reduction techniques for verification with non-rigorous error bounds. We design experiments based on the proper orthogonal decomposition technique for finding reduced order models. We find that reduced order models are particularly effective and precise whenever a suitable reduced order model can be found in the first place. We attempt to characterize these models and provide future directions for reduced order modeling.
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Chou, Y., Chen, X., Sankaranarayanan, S. (2017). A Study of Model-Order Reduction Techniques for Verification. In: Abate, A., Boldo, S. (eds) Numerical Software Verification. NSV 2017. Lecture Notes in Computer Science(), vol 10381. Springer, Cham. https://doi.org/10.1007/978-3-319-63501-9_8
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DOI: https://doi.org/10.1007/978-3-319-63501-9_8
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