Self-Learning Active Metamaterials: A Local Learning Framework for Non-reciprocal Linear Flow Networks

Candás, Raúl; Stern, Menachem

doi:10.1109/metamaterials65622.2025.11174117

We present a framework for physical local learning in metamaterials based on a linear flow network with symmetric and antisymmetric components. This model extends previous work on steady-state networks, incorporating asymmetric interactions to broaden the scope and potential applications of local learning procedures.

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Publisher	New York: IEEE
Persistent URL	doi.org/10.1109/metamaterials65622.2025.11174117
Organisation	Learning Machines
Citation APA APA Style APA-ALL Style AAA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Candás, R.& Stern, M. (2025, September). Self-Learning Active Metamaterials: A Local Learning Framework for Non-reciprocal Linear Flow Networks. Nineteenth International Congress on Artificial Materials for Novel Wave Phenomena (Metamaterials), 2025.https://doi.org/10.1109/metamaterials65622.2025.11174117

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