论文标题
最大熵快照抽样，以减少基础生成
Maximum Entropy Snapshot Sampling for Reduced Basis Generation
论文作者
论文摘要
快照的后端减少了动态系统的基础方法，通常依赖于矩阵的奇异值分解，该矩阵的列是高效率解矢量。这里开发了一个替代基础生成框架。提倡的最大熵快照采样（MYS）通过利用适合量化动态稳定性概念的数量来识别有关系统演化的基本信息的快照。最大熵快照采样可直接减少快照数量。然后，通过任何正定化过程在生成的快照样本中获得降低的基础。最大的熵采样策略由刚性数学基础支持，它在计算上是有效的，并且本质上是自动化且易于实现的。
Snapshot back-ended reduced basis methods for dynamical systems commonly rely on the singular value decomposition of a matrix whose columns are high-fidelity solution vectors. An alternative basis generation framework is developed here. The advocated maximum entropy snapshot sampling (MESS) identifies the snapshots that encode essential information regarding the system's evolution, by exploiting quantities that are suitable for quantifying a notion of dynamical stability. The maximum entropy snapshot sampling enables a direct reduction of the number of snapshots. A reduced basis is then obtained with any orthonormalization process on the resulting reduced sample of snapshots. The maximum entropy sampling strategy is supported by rigid mathematical foundations, it is computationally efficient, and it is inherently automated and easy to implement.
