Symplectic Model Order Reduction with Non-Orthonormal Bases
AbstractParametric high-fidelity simulations are of interest for a wide range of applications. However, the restriction of computational resources renders such models to be inapplicable in a real-time context or in multi-query scenarios. Model order reduction (MOR) is used to tackle this issue. Recently, MOR is extended to preserve specific structures of the model throughout the reduction, e.g., structure-preserving MOR for Hamiltonian systems. This is referred to as symplectic MOR. It is based on the classical projection-based MOR and uses a symplectic reduced order basis (ROB). Such an ROB can be derived in a data-driven manner with the Proper Symplectic Decomposition (PSD) in the form of a minimization problem. Due to the strong nonlinearity of the minimization problem, it is unclear how to efficiently find a global optimum. In our paper, we show that current solution procedures almost exclusively yield suboptimal solutions by restricting to orthonormal ROBs. As a new methodological contribution, we propose a new method which eliminates this restriction by generating non-orthonormal ROBs. In the numerical experiments, we examine the different techniques for a classical linear elasticity problem and observe that the non-orthonormal technique proposed in this paper shows superior results with respect to the error introduced by the reduction. View Full-Text
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Buchfink, P.; Bhatt, A.; Haasdonk, B. Symplectic Model Order Reduction with Non-Orthonormal Bases. Math. Comput. Appl. 2019, 24, 43.
Buchfink P, Bhatt A, Haasdonk B. Symplectic Model Order Reduction with Non-Orthonormal Bases. Mathematical and Computational Applications. 2019; 24(2):43.Chicago/Turabian Style
Buchfink, Patrick; Bhatt, Ashish; Haasdonk, Bernard. 2019. "Symplectic Model Order Reduction with Non-Orthonormal Bases." Math. Comput. Appl. 24, no. 2: 43.
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