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Neural Network Detection of Shockwaves

Contemporary neural networks simulate the operation of brain subsystems with substantially higher e delity than was possible a few years ago. These improvements offer new opportunities to use the cognitive competence of neural networks in e uid mechanics. One such opportunity is developed by taking concepts from their origin in neuroscience through a neural network to the e uid mechanics. The discussion focuses on one brain subsystem (the “ What” pathway of the visual system ), adaptsan existing neural network modelofthevisual system (theboundary contour system/feature contour system ), and shows how a e owe eld scalar can excite a pattern of simulated neural activity. The neural network uses three types of shunting neurons found in the retina, lateral geniculate nucleus, and cortical area V1. When shown the density e eld of a supersonic channel e ow, certain neurons in the neural network become active when a shock passes nearby. These active neurons provide a pattern of neural activity that could feed into higher-level What pathway neural networks to make more sophisticated inferences about structures present in a e ow. The challenges posed by embedding the current neural network in these higher-level brain structures suggest a promising research agenda.