2018/12/22 by M. A. Khodkar, Mohammad Amin Khodkar, Khodkar, M. A. +4
Engineering · Mathematics · Physics and Astronomy · #Applied mathematics #Artificial intelligence #Chaotic #Chaotic Dynamics (nlin.CD) #Computer science #Dynamic mode decomposition #Dynamical Systems (math.DS) #Embedding #FOS: Mathematics #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Fluid Dynamics and Turbulent Flows #Lattice Boltzmann Simulation Studies #Mathematical analysis #Mathematics #Mechanics #Meteorology #Mode (computer interface) #Model Reduction and Neural Networks #Observable #Operator (biology) #Optimization and Control (math.OC) #Physics #Quantum mechanics #Statistical physics #Transfer operator #Turbulence #math.DS #math.OC #nlin.CD #physics.flu-dyn
paper · pdf · doi:10.48550/arxiv.1812.09438
Figures and texts need to be revised
openalex publication_date 2018/12/22 · openalex created_date 2019/01/01 · arxiv created 2019/03/02 · arxiv updated 2019/03/05 · openalex updated_date 2026/07/28
We show the skills of a data-driven low-dimensional linear model in predicting the spatio-temporal evolution of turbulent Rayleigh-Bénard convection. The model is based on dynamic mode decomposition with delay-embedding, which provides a data-driven finite-dimensional approximation to the system's Koopman operator. The model is built using vector-valued observables from direct numerical simulations, and can provide accurate predictions. Similar high prediction skills are found for the Kuramoto-Sivashinsky equation in the strongly-chaotic regimes.