Citing Articles For: Physical learning beyond the quasistatic limit

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Proc. Natl. Acad. Sci. U.S.A. 123 e2604421123 (2026)

Learning to guide non-equilibrium dynamics using local contrastive updates

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Self-organizing memristive networks as physical learning systems

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Topological mechanical neural networks as classifiers through in situ backpropagation learning

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Equilibrium Propagation for Dissipative Dynamics

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Kinetic frustration enables single-molecule computation

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The Journal of Chemical Physics 162 134102 (2025)

Microscopic Imprints of Learned Solutions in Tunable Networks

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Phys. Rev. X 15 031056 (2025)

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Universal approximation theorem for nonlinear resistive networks

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Temporal Contrastive Learning through implicit non-equilibrium memory

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Physical neural networks with self-learning capabilities

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Machine learning without a processor: Emergent learning in a nonlinear analog network

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Proc. Natl. Acad. Sci. U.S.A. 121 e2319718121 (2024)

Physical effects of learning

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Phys. Rev. E 109 024311 (2024)

Experimental demonstration of coupled learning in elastic networks

Lauren E. Altman, Menachem Stern, Andrea J. Liu, and Douglas J. Durian

Phys. Rev. Applied 22 024053 (2024)

Training coupled phase oscillators as a neuromorphic platform using equilibrium propagation

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DNA as a universal chemical substrate for computing and data storage

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Training self-learning circuits for power-efficient solutions

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Training precise stress patterns

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Soft Matter 19 2120 (2023)

The role of the teacher-student relationship in the innovation of education: Interdisciplinary review of the issue

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Scientific Bulletin of Mukachevo State University Series “Pedagogy and Psychology” 9 72 (2023)

Learning by non-interfering feedback chemical signaling in physical networks

Vidyesh Rao Anisetti, B. Scellier, and J. M. Schwarz

Phys. Rev. Research 5 023024 (2023)

Learning to self-fold at a bifurcation

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Phys. Rev. E 107 025001 (2023)

Learning Without Neurons in Physical Systems

Menachem Stern and Arvind Murugan

Annu. Rev. Condens. Matter Phys. 14 417 (2023)

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Articles Citing

Physical learning beyond the quasistatic limit

Learning by Training: Emergent Physical Memory from Cyclically Tuning Disordered Sphere Packings

Thermally Robust Cooperative Function in Mechanical Networks

Unsupervised and probabilistic learning with Contrastive Local Learning Networks: The Restricted Kirchhoff Machine

Double descent without digital computation

Learning to guide non-equilibrium dynamics using local contrastive updates

Self-organizing memristive networks as physical learning systems

Topological mechanical neural networks as classifiers through in situ backpropagation learning

Equilibrium Propagation for Dissipative Dynamics

Kinetic frustration enables single-molecule computation

Microscopic Imprints of Learned Solutions in Tunable Networks

Physical Networks Become What They Learn

Universal approximation theorem for nonlinear resistive networks

Temporal Contrastive Learning through implicit non-equilibrium memory

Physical neural networks with self-learning capabilities

Machine learning without a processor: Emergent learning in a nonlinear analog network

Physical effects of learning

Experimental demonstration of coupled learning in elastic networks

Training coupled phase oscillators as a neuromorphic platform using equilibrium propagation

DNA as a universal chemical substrate for computing and data storage

Training self-learning circuits for power-efficient solutions

Training precise stress patterns

The role of the teacher-student relationship in the innovation of education: Interdisciplinary review of the issue

Learning by non-interfering feedback chemical signaling in physical networks

Learning to self-fold at a bifurcation

Learning Without Neurons in Physical Systems

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