ABSTRACT Parametric model order reduction by matrix interpolation allows for efficient prediction of the behavior of dynamic systems without requiring knowledge about the underlying parametric dependency. Within this approach, reduced models are first sampled and then made consistent with each other by transforming the underlying reduced bases. Finally,
Sebastian Resch‐Schopper +2 more
wiley +1 more source
Enhanced Krylov Methods for Molecular Hamiltonians: Reduced Memory Cost and Complexity Scaling via Tensor Hypercontraction. [PDF]
Wang Y, Luo M, Reumann M, Mendl CB.
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Prediction of the transient coolant jet released from the nose cone at supersonic flow via machine learning. [PDF]
Basem A +9 more
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Reliability Assessment of High-Speed Train Gearbox Based on Digital Twin and WHO-WPHM. [PDF]
Wang T +6 more
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A Reduced Order Model for Sea Water Intrusion Simulation Using Proper Orthogonal Decomposition. [PDF]
Geranmehr M +4 more
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A segregated reduced-order model of a pressure-based solver for turbulent compressible flows. [PDF]
Zancanaro M +3 more
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Discontinuity Characterization and Low-Complexity Smoothing in RF-PA Polynomial Piecewise Modeling. [PDF]
Pedrosa C +5 more
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m6AConquer: a consistently quantified and orthogonally validated database for the N6-methyladenosine (m6A) epitranscriptome. [PDF]
Zhao X +6 more
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