Resultant force on grains of a real sand dune

This repository contains the dataaset used to train a convolutional neural network (CNN) designed to measure the resultant forces acting on a barchan dune. The model has been trained using numerical data to predict force distributions based on the morphological features of dunes. In our research, we developed a groundbreaking method that leverages deep learning to estimate the forces acting on real barchan dunes at grain scale. This method combines image data from subaqueous experiments with numerical simulations, offering a novel approach to granular mechanics measurement. The trained CNN model is capable of predicting force distributions on dunes from their morphological features, even when applied to dune configurations not seen during training.