IEEE

Efficient Surrogate Modeling for High-Dimensional Parametric Problems Based on Multi-Level Fidelity Simulation Combined with Incremental Learning

For high-dimensional parametric problems, data-driven surrogate models can be generated from simulation or experimental data to quickly evaluate quantities of interest, in support of applications such ...
IEEE

Proper Orthogonal Decomposition for Model Order Reduction of High Dimensional Parametric Problems with Varying Geometries Based on the Interface Adaptive Mesh

Abstract: The proper orthogonal decomposition (POD) is commonly applied for the model order reduction (MOR) of parametric problems. The method generates a reduced basis from the left singular vectors ...