Patterns (Jun 2022)
Multi-input convolutional network for ultrafast simulation of field evolvement
Abstract
Summary: There is a compelling need for the regression capability of mapping the initial field and applied conditions to the evolved field, e.g., given current flow field and fluid properties predicting next-step flow field. Such a capability can provide a maximum to full substitute of a physics-based model, enabling fast simulation of various field evolvements. We propose a conceptually simple, lightweight, but powerful multi-input convolutional network (ConvNet), yNet, that merges multi-input signals by manipulating high-level encodings of field/image input. yNet can significantly reduce the model size compared with its ConvNet counterpart (e.g., to only one-tenth for main architecture of 38-layer depth) and is as much as six orders of magnitude faster than a physics-based model. yNet is applied for data-driven modeling of fluid dynamics, porosity evolution in sintering, stress field development, and grain growth. It consistently shows great extrapolative prediction beyond training datasets in terms of temporal ranges, spatial domains, and geometrical shapes. The bigger picture: In physical sciences and engineering, the convolutional network (ConvNet) has been used increasingly to simulate the evolvement of physical fields, e.g., flow field evolvement. Physical field data are fed as images, and ConvNet treats the field evolvement as a field-to-field/image-to-image regression problem, i.e., building the mapping from the input flow field to the evolved flow field. The ConvNet, when trained, can be a cheap substitute for physics-based models, enabling fast simulation of field evolvement. However, a big challenge still lies in incorporating conditions that dictate field evolvement, e.g., fluid properties associated with fluid dynamics. We propose a light multi-input ConvNet as a general-purpose, multi-input, image-to-image regression tool. Its simplicity and usefulness are demonstrated by modeling various condition-dependent field evolvements and developments. Large- and extreme-scale simulations are also performed based on its computational superiority.
