Exploring the efficacy of a hybrid approach with modal decomposition over fully deep learning models for flow dynamics forecasting

arXiv:2404.17884v1 Announce Type: cross
Abstract: Fluid dynamics problems are characterized by being multidimensional and nonlinear, causing the experiments and numerical simulations being complex, time-consuming and monetarily expensive. In this sense, there is a need to find new ways to obtain data in a more economical manner. Thus, in this work we study the application of time series forecasting to fluid dynamics problems, where the aim is to predict the flow dynamics using only past information. We focus our study on …

abstract arxiv cs.lg data deep learning dynamics flow fluid dynamics forecasting hybrid hybrid approach modal multidimensional numerical physics.flu-dyn sense simulations type