  The capability of multi-input field-to-field regression, i.e. mapping the
initial field and applied conditions to the evolved field, is appealing,
enabling ultra-fast physics-free simulation of various field evolvements across
many disciplines. We hereby propose a y-shaped multi-input deep convolutional
network, yNet, which can effectively account for combined effects of multiple
mixed inputs on output field. The proposed yNet is applied to the simulation of
porosity evolution in selective lasering sintering (SLS). Upon testing, yNet
can simulate nearly identical porosity evolution and development to the
physics-based model, with a 99.13% morphological similarity for various SLS
conditions. We then effortlessly boost the porosity simulation capability to
the realistic, full-component level. yNet is generally applicable to simulating
various structural/morphological evolutions and other condition-concerned,
continuous field evolvements even with spatially and/or temporally non-uniform
evolving kinetics. Once trained, the light-weight yNet can be distributed
easily and ran with limited computational resource while reducing computation
time to a split second. It thus may have a transformative impact by
democratizing the capability of ultra-fast and extreme-scale simulation of
various field evolvements.
