5 papers
Dynamics of a Data-Driven Low-Dimensional Model of Turbulent Minimal Pipe Flow
C. Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham
The simulation of turbulent flow requires many degrees of freedom to resolve all the relevant times and length scales. However, due to the dissipative nature of the Navier-Stokes e…
Extracting dominant dynamics about unsteady base flows
Alec J. Linot, Barbara Lopez-Doriga, Yonghong Zhong +1
A wide range of techniques exist for extracting the dominant flow dynamics and features about steady, or periodic base flows. However, there have been limited efforts in extracting…
Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flow
Carlos E. Pérez De Jesús, Alec J. Linot, Michael D. Graham
Data-driven reduced-order models of the dynamics of complex flows are important for tasks related to design, understanding, prediction, and control. Many flows obey symmetries, and…
Hierarchical equivariant graph neural networks for forecasting collective motion in vortex clusters and microswimmers
Alec J. Linot, Haotian Hang, Eva Kanso +1
Data-driven modeling of collective dynamics is a challenging problem because emergent phenomena in multi-agent systems are often shaped by long-range interactions among individuals…
Data-driven prediction of large-scale spatiotemporal chaos with distributed low-dimensional models
C. Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham
Complex chaotic dynamics, seen in natural and industrial systems like turbulent flows and weather patterns, often span vast spatial domains with interactions across scales. Accurat…