Convolutional autoencoders for the reconstruction of three-dimensional interfacial multiphase flows
This work provides guidance for the multiphase flow community on using autoencoders to enable more efficient training of temporal models, though it is incremental in nature.
The study investigated autoencoders for reconstructing three-dimensional multiphase flows, examining interface representation choices and using synthetic and simulation data to establish best practices for dimensionality reduction.
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.