Predicting Energy Budgets in Droplet Dynamics: A Recurrent Neural Network Approach

arXiv:2403.16144v1 Announce Type: cross
Abstract: Neural networks in fluid mechanics offer an efficient approach for exploring complex flows, including multiphase and free surface flows. The recurrent neural network, particularly the Long Short-Term Memory (LSTM) model, proves attractive for learning mappings from transient inputs to dynamic outputs. This study applies LSTM to predict transient and static outputs for fluid flows under surface tension effects. Specifically, we explore two distinct droplet dynamic scenarios: droplets with diverse initial shapes impacting with solid surfaces, …

abstract arxiv budgets cs.lg dynamic dynamics energy free inputs long short-term memory lstm memory network networks neural network neural networks physics.flu-dyn recurrent neural network study surface type