Large-Scale Synthesis of Metal Additively-Manufactured Microstructures Using Markov Random Fields

Accelerate microstructure evolution simulation using graph neural networks with adaptive spatiotemporal resolution

“…All neural network operators were built from multi-layer perceptrons with two hidden layers and layer normalization [64] between them. Skip connections [65] of the form x ← x + f(x) were adopted in the message-passing layers in addition to equation (10). The critical message-passing steps in equation (10) allow the GNN to transmit and aggregate information along connected edges, and each additional message-passing layer builds up a more complex dependence on vertices farther away.…”

“…Skip connections [65] of the form x ← x + f(x) were adopted in the message-passing layers in addition to equation (10). The critical message-passing steps in equation (10) allow the GNN to transmit and aggregate information along connected edges, and each additional message-passing layer builds up a more complex dependence on vertices farther away. The number of message-passing layers K required to obtain accurate predictions is then related to a few factors, including the effective instantaneous interaction distance, and the levels of spatial and temporal downs-sampling.…”

“…As such, extensive efforts have been devoted to the development of acceleration techniques for microstructure simulations such as parallel computing and adaptive mesh refinement (AMR) [5][6][7], but the gap remains significant. Notably the Markov Random Fields (MRF) method, a powerful and scalable computational approach, has been recently developed for accurate 2/3D polycrystalline reconstruction and spatiotemporal grain-growth predictions based on experimental inputs [8,9] and can be integrated into component-sized design workflow [10].…”