Model-reduction techniques aim to reduce the computational complexity of simulating dynamical systems by applying a (Petrov-)Galerkin projection process that enforces the dynamics to evolve in a low-dimensional subspace of the original state space. Frequently, the resulting reduced-order model (ROM) violates intrinsic physical properties of the original full-order model (FOM) (e.g., global conservation, Lagrangian structure, state-variable bounds) because the projection process does not generally ensure preservation of these properties. However, in many applications, ensuring the ROM preserves such intrinsic properties can enable the ROM to retain physical meaning and lead to improved accuracy and stability properties. In this work, we present a general constrained-optimization formulation for projection-based model reduction that can be used as a template to enforce the ROM to satisfy specific properties on the kinematics and dynamics. We introduce constrained-optimization formulations at both the time-continuous (i.e., ODE) level, which leads to a constrained Galerkin projection, and at the time-discrete level, which leads to a least-squares Petrov-Galerkin (LSPG) projection, in the context of linear multistep schemes. We demonstrate the ability of the proposed formulations to equip ROMs with desired properties such as global energy conservation and bounds on the total variation.
翻译:减少模型技术的目的是降低模拟动态系统的计算复杂性,办法是应用一个(Petrov-)Galerkin预测程序,使动态在原始状态空间的低维次空间中演化,使动态得以在原始状态空间的低维次空间中演化,从而降低模型的计算复杂性。结果产生的减少序列模型(ROM)经常违反原全序模型(FOM)的内在物理特性(例如全球保护、Lagrangian结构、国家可变界限),因为预测程序一般不能确保这些特性的维护。然而,在许多应用中,确保ROM保存这些内在特性能够使ROM保持物理意义,并导致提高准确性和稳定性。在这项工作中,我们提出了一个基于预测的基于模型(ROM)(例如全球保护、Lagangian结构、国家可变质框架)的总体限制优化模型,可以用作执行该模型的模板,以满足运动体和动态中的具体特性。我们在时间-持续(e.ODE)层次上引入限制优化的配置公式,导致受限制的Galkin投影素投影,并在时间-crekin系统总变异性水平上显示PILS的预测能力。