Data-Driven Approach for Modeling Coagulation Kinetics

Lukashevich, D.; Ovchinnikov, George; Tyukin, Ivan; Matveev, S. A.; Brilliantov, Nikolai V.

doi:10.1007/s10598-023-09574-5

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…In a recent work, the authors also demonstrated the ability of the DMD method to predict the temporal evolution of complex systems. Lukashevich et al [38] predicted the evolution of the aggregation kinetics system using a small number of snapshots based on DMD. Duan [39] employed DMD technology to learn temporal features, enabling the prediction of full-order solutions at new time points.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Reduced-Order Modeling Based on Parametrized Proper Orthogonal Decomposition

Li,

Pan,

Zhou

et al. 2023

Energies

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A new non-intrusive reduced-order modeling method based on space-time parameter decoupling for parametrized time-dependent problems is proposed. This method requires the preparation of a database comprising high-fidelity solutions. The spatial bases are extracted from the database through first-level proper orthogonal decomposition (POD). The algebraic relationship between the time trajectory/parameter positions and the projection coefficient is described by the linear superposition of the second-level POD bases (temporal bases) and the second-level projection coefficients (parameter-dependent coefficients). This decomposition strategy decouples the space-time parameter effects, providing a stable foundation for fast predictions of parametrized time-dependent problems. The mappings between the parameter locations and the parameter-dependent coefficients are approximated as Gaussian process regression (GPR) models. The accuracy and efficiency of the PPOD-ROM are demonstrated through two numerical examples: flows past a cylinder and turbine flows with a clocking effect.

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Section: Introductionmentioning

confidence: 99%