Karen Willcox

TL;DR: The goal is to combine kinetic and kinematic data to examine translational motions during microgravity adaptations to encourage fine-control motions as these reduce the risk of injury and increase controllability.

TL;DR: Model reduction aims to reduce the computational burden by generating reduced models that are faster and cheaper to simulate, yet accurately represent the original large-scale system behavior as mentioned in this paper. But model reduction of linear, nonparametric dynamical systems has reached a considerable level of maturity, as reflected by several survey papers and books.

TL;DR: A new method for performing a balanced reduction of a high-order linear system is presented, which combines the proper orthogonal decomposition and concepts from balanced realization theory and extends to nonlinear systems.

TL;DR: In many situations across computational science and engineering, multiple computational models are available that describe a system of interest as discussed by the authors, and these different models have varying evaluation costs, i.e.

TL;DR: In this paper, proper orthogonal decomposition for incomplete (gappy) data for compressible external aerodynamic problems has been demonstrated successfully in the first time, and the sensitivity of flow reconstruction results to available measurements and to experimental error is analyzed.