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A non-intrusive model order reduction method based on nonlinear optimization for parameterized Stokes problems
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DOI：10.1016/j.cam.2025.117283.png)
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Abstract
En 中文
This paper presents a non-intrusive model order reduction method based on nonlinear optimization for steady parameterized Stokes problems. To achieve this, we employ a weighted loss function to balance the velocity and pressure outputs to obtain a non-intrusive, data-driven algorithm utilizing only output samples. Moreover, we derive the gradients of the objective function with respect to the reduced-order matrices by resorting to the parameter-separable forms of reduced-model quantities. To enhance computational efficiency, our framework employs a two-stage offline-online decomposition. In the offline stage, we leverage gradient information to develop an optimization algorithm that computes optimal approximations for reduced-order matrices. In the online stage, the outputs can be quickly estimated for new parameter values using the reduced-order model obtained from the offline phase. Finally, we present numerical experiments to validate the effectiveness of this method, especially to demonstrate its capability to produce highly accurate approximation results.
Keywords:
Steady parameterized Stokes problems
Model order reduction
Data-driven
Nonlinear optimization
Journal
J
IF:
2.6
Papers:
336
Citations:
0