arXiv:2606. 15443v1 Announce Type: cross Abstract: Physical learning methods train physical networks to perform computational tasks using only local update rules, exploiting the physics of the system to handle the global transfer of information.
By Joshua A. McGinnis, Xinbo Li, Yoichiro Mori
arXiv:2608. 00097v1 Announce Type: cross Abstract: Physical learning rules such as equilibrium propagation (EP), coupled learning (CL), and adjoint coupled learning (AL) train resistive networks through local measurements.
By Bijaya Dangol
arXiv:2602. 03670v2 Announce Type: replace-cross Abstract: Equilibrium Propagation (EP) is a physics-inspired learning algorithm that uses stationary states of a dynamical system both for inference and learning.
By Antonino Emanuele Scurria, Dimitri Vanden Abeele, Bortolo Matteo Mognetti, Serge Massar
The paper investigates how two independent inductive biases—one from the circuit’s invariance under conductance rescaling and one from the learning rule’s conservation of a mass quantity—affect what a physical learning system remembers. By separating these effects, the authors show that when every element is trainable, the initialization scale has negligible influence on the learned function, whereas a single untrainable element can cause the function to shift significantly with initialization. They further demonstrate that the conservation law does not protect memory but instead influences solution quality, with adjoint coupled learning (AL) generally performing worse than equilibrium propagation (EP) and coupled learning (CL) in small circuits.
whyItMatters":"The study clarifies that only the circuit’s structural bias, not the rule’s conservation property, determines memory retention in physical learning systems."
By Bijaya Dangol
arXiv:2609.05808v1 Announce Type: cross
Abstract: In situ adjoint training extracts parameter gradients directly from measurement, but has so far been limited to reciprocal or restricted systems. Her...
By William Tuxbury, Zin Lin
arXiv:2608.30778v1 Announce Type: new
Abstract: Physical learning lets a trainable material or network use its own physical response to carry error signals, reducing the need for a separately program...
By Ruiwu Niu, Xiaowen Bi, Micha\"el Antonie van Wyk