Sequential Learning and Catastrophic Forgetting in Differentiable Resistor Networks

ArXi:2605.01383v1 Announce Type: new Differentiable physical networks provide a simple setting in which learning can be studied through the interaction between trainable parameters and physical equilibrium constraints. We investigate sequential learning in differentiable resistor networks governed by Kirchhoff's laws. Although individual input--output mappings can be learned by gradient-based adjustment of edge conductances, sequential