Convolutional autoencoders for the reconstruction of three-dimensional interfacial multiphase flows

Abstract: In this work, we perform a comprehensive investigation of autoencoders for reduced-order modeling of three-dimensional multiphase flows. Focusing on the accuracy of reconstructing multiphase flow volume/mass fractions with a standard convolutional architecture, we examine the advantages and disadvantages of different interface representation choices (diffuse, sharp, level set). We use a combination of synthetic data with non-trivial interface topologies and high-resolution simulation data of multiphase homogeneous isotropic turbulence for training and validation. This study clarifies the best practices for reducing the dimensionality of multiphase flows via autoencoders. Consequently, this paves the path for uncoupling the training of autoencoders for accurate reconstruction and the training of temporal or input/output models such as neural operators (e.g., FNOs, DeepONets) and neural ODEs on the lower-dimensional latent space given by the autoencoders. As such, the implications of this study are significant and of interest to the multiphase flow community and beyond.