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Accelerating Part-Scale Simulation in Liquid Metal Jet Additive Manufacturing via Operator Learning
Predicting part quality for additive manufacturing (AM) processes requires high-fidelity numerical simulation of partial differential equations (PDEs) governing process multiphysics on a scale of minimum manufacturable features.
Mutual Information for Explainable Deep Learning of Multiscale Systems
Timely completion of design cycles for complex systems ranging from consumer electronics to hypersonic vehicles relies on rapid simulation-based prototyping.
GINNs: Graph-Informed Neural Networks for Multiscale Physics
We introduce the concept of a Graph-Informed Neural Network (GINN), a hybrid approach combining deep learning with probabilistic graphical models (PGMs) that acts as a surrogate for physics-based representations of multiscale and multiphysics systems.
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