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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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