Dynamical frustration in parametrically driven space-time metamaterials produces topologically protected unidirectional phase dislocations that self-organize into synchronized defects in 2D.
Metamaterials that learn to change shape , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
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Simulations show physical neural networks need nonlinearity, amplification, and suppression for learning, with physically plausible circuit designs presented.
citing papers explorer
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Dynamical frustration in space-time metamaterials
Dynamical frustration in parametrically driven space-time metamaterials produces topologically protected unidirectional phase dislocations that self-organize into synchronized defects in 2D.
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Physical Neural Networks Need Nonlinearity, Amplification, and Suppression for Learning
Simulations show physical neural networks need nonlinearity, amplification, and suppression for learning, with physically plausible circuit designs presented.