Speaker
Description
To approximate n-point probability functions, different techniques are feasible. Machine learning has proven to be very effective in a wide variety of appplications, such as statistical homogenization, where n-point probability functions play a significant role.
To specifically extract 2-point probability functions from isotropic two-phase microstructures, we achieved good results by employing a coupled neural network structure that combines a convolutional (CNN) and a fully connected network (FCNN).In a second approach, we propose a neural operator that not only approximates these probability functions but also captures the infinite dimensionality of the target function space, in contrast to a classical artificial neural network (ANN) approach.
