The basis generation in reduced order modeling usually requires multiple high-fidelity large-scale simulations that could take a huge computational cost. In order to accelerate these numerical simulations, we introduce a FOM/ROM hybrid approach in this paper. It is developed based on an a posteriori error estimation for the output approximation of the dynamical system. By controlling the estimated error, the method dynamically switches between the full-order model and the reduced-oder model generated on the fly. Therefore, it reduces the computational cost of a high-fidelity simulation while achieving a prescribed accuracy level. Numerical tests on the non-parametric and parametric PDEs illustrate the efficacy of the proposed approach.
翻译:以减序模型生成基数通常需要多度高纤维化大型模拟,这可能需要巨大的计算成本。为了加速这些数字模拟,我们在本文件中引入了FOM/ROM混合法。它是根据对动态系统产出近似值的事后误差估计开发的。通过控制估计误差,全序模型和在飞上生成的降值模型之间的方法动态开关。因此,它降低了高纤维化模拟的计算成本,同时达到了规定的精确度。非参数和参数PDE的数值测试显示了拟议方法的功效。