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The capability of multi-input field-to-field regression, i.e. mapping theinitial field and applied conditions to the evolved field, is appealing,enabling ultra-fast physics-free simulation of various field evolvements acrossmany disciplines. We hereby propose a y-shaped multi-input deep convolutionalnetwork, yNet, which can effectively account for combined effects of multiplemixed inputs on output field. The proposed yNet is applied to the simulation ofporosity evolution in selective lasering sintering (SLS). Upon testing, yNetcan simulate nearly identical porosity evolution and development to thephysics-based model, with a 99.13% morphological similarity for various SLSconditions. We then effortlessly boost the porosity simulation capability tothe realistic, full-component level. yNet is generally applicable to simulatingvarious structural/morphological evolutions and other condition-concerned,continuous field evolvements even with spatially and/or temporally non-uniformevolving kinetics. Once trained, the light-weight yNet can be distributedeasily and ran with limited computational resource while reducing computationtime to a split second. It thus may have a transformative impact bydemocratizing the capability of ultra-fast and extreme-scale simulation ofvarious field evolvements.

yNet: a multi-input convolutional network for ultra-fast simulation of field evolvement