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The Hong Kong University of Science and Technology (
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Aggregation in map generalization proves to be significantly more complex than simplification, revealing critical challenges for graph-based deep learning methods.
Randomly initialized neural nets can solve high-dimensional integro-differential equations like neutron transport faster and more stably than both physics-informed neural networks and traditional deterministic methods.