DiffeoMorph learns distributed agent protocols to morph into complex 3D shapes from minimal initial conditions via equivariant GNNs and rotation-invariant Zernike loss.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Active matter is evolving from spontaneous collective dynamics through nonreciprocal mechanics toward learning-based smart matter, where learning acts as a new form of emergence that may replace explicit control.
citing papers explorer
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DiffeoMorph: Learning to Morph 3D Shapes Using Differentiable Agent-Based Simulations
DiffeoMorph learns distributed agent protocols to morph into complex 3D shapes from minimal initial conditions via equivariant GNNs and rotation-invariant Zernike loss.
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From Active to Odd to Smart Matter
Active matter is evolving from spontaneous collective dynamics through nonreciprocal mechanics toward learning-based smart matter, where learning acts as a new form of emergence that may replace explicit control.