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arXiv 2206.01792math.NAcs.NA
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基于离散经验插值的非线性动力系统保梯度超降阶方法

Gradient-preserving hyper-reduction of nonlinear dynamical systems via discrete empirical interpolation

Cecilia Pagliantini, Federico Vismara

AI总结:

针对非线性参数动力系统超降阶易丧失梯度结构的问题,提出基于DEIM的保梯度超降阶方法,结合自适应低秩校正策略,在保留梯度结构的同时实现计算加速与误差收敛。

摘要:

本工作针对以梯度场为特征的非线性参数动力系统(如哈密顿系统和梯度流)提出了一种超降阶方法。梯度结构与不变量守恒或耗散相关,因此在描述系统物理特性方面发挥着关键作用。传统的非线性梯度场超降阶方法虽能得到高效近似,但会丧失梯度结构。我们聚焦于哈密顿梯度,提出首先将映射到合适降阶空间的哈密顿量非线性部分分解为d项之和,每一项都具有对系统状态的稀疏依赖特性。随后,通过对所得到的d值非线性函数的雅可比矩阵进行离散经验插值(DEIM),获得超降阶近似。由此得到的超降阶模型保留了梯度结构,且其计算复杂度与全模型的规模无关。此外,先验误差估计表明,超降阶模型收敛于降阶模型,且哈密顿量可被渐近保持。当非线性哈密顿梯度不具备全局可约性时,即其演化需要高维DEIM近似空间时,我们采用一种自适应策略。该策略通过对DEIM基进行低秩校正来更新超降阶哈密顿量。数值测试证明了所提方法适用于一般非线性算子,且相较于全模型和降阶模型,运行速度得到了提升。

英文摘要:

This work proposes a hyper-reduction method for nonlinear parametric dynamical systems characterized by gradient fields such as Hamiltonian systems and gradient flows. The gradient structure is associated with conservation of invariants or with dissipation and hence plays a crucial role in the description of the physical properties of the system. Traditional hyper-reduction of nonlinear gradient fields yields efficient approximations that, however, lack the gradient structure. We focus on Hamiltonian gradients and we propose to first decompose the nonlinear part of the Hamiltonian, mapped into a suitable reduced space, into the sum of d terms, each characterized by a sparse dependence on the system state. Then, the hyper-reduced approximation is obtained via discrete empirical interpolation (DEIM) of the Jacobian of the derived d-valued nonlinear function. The resulting hyper-reduced model retains the gradient structure and its computationally complexity is independent of the size of the full model. Moreover, a priori error estimates show that the hyper-reduced model converges to the reduced model and the Hamiltonian is asymptotically preserved. Whenever the nonlinear Hamiltonian gradient is not globally reducible, i.e. its evolution requires high-dimensional DEIM approximation spaces, an adaptive strategy is performed. This consists in updating the hyper-reduced Hamiltonian via a low-rank correction of the DEIM basis. Numerical tests demonstrate the applicability of the proposed approach to general nonlinear operators and runtime speedups compared to the full and the reduced models.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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