3 papers
physics.flu-dyn2024
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…
nlin.CD2024
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…
cs.LG2020
Deep learning to discover and predict dynamics on an inertial manifold
Alec J. Linot, Michael D. Graham
A data-driven framework is developed to represent chaotic dynamics on an inertial manifold (IM), and applied to solutions of the Kuramoto-Sivashinsky equation. A hybrid method comb…