Periodic Environmental Forcing Shapes the Stability of Complex Ecological Networks

What the math gives to ML

The paper provides a stability theory for linearized networks whose interaction matrix varies periodically in time. Its main transferable mechanism is that slow forcing is governed by the time average of the instantaneous rightmost spectral edge, whereas sufficiently rapid forcing can stabilize a system that is unstable at every static snapshot. A neural-network implementation would periodically modulate an optimizer, recurrent transition matrix, or residual Jacobian and monitor the resulting Floquet growth rate, using the predicted slow- and fast-forcing stability boundaries as falsifiable design criteria.