I've finally perfected the "Chase Nonlinear Feedback" (CNFB) method for the modeling of nonlinear networks. And it works amazingly well. Has the accuracy of high-order integration methods with less computational burden.

Can simulate diodes, triodes, pentodes, etc. Far less error-prone than other methods (like K-method or DK-method, etc.) as you don't need to enter large matrices or tables.

It works on the principle that nonlinear devices can be thought of as linear devices with nonlinear feedback. You compute the states of a linear network and apply nonlinear feedback to get the output. It's also inherently stable. If the analog version of the network is stable, the CNFB implementation is stable.

The plot below is a simple example. This is a single-sided diode clipper with "memory" (the memory being a capacitor across the diode). The dotted line uses classic nonlinear ODE techniques solving the network using Trapezoidal Rule integration. The dashed line uses the CNFB method. The results are virtually identical but the CNFB method executes in about 60% the time (12 operations per loop vs. 20). As the number of nodes in a network increases the computational advantage increases proportionally.

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Here's a more complex example. This is a plot of a 6L6GC push-pull power amp into a reactive load (blue) compared to the same power amp simulated in SPICE (red). Doing this with conventional methods (nodal K, DK, WDF, etc.) induces major thinky-pain. I did this with the CNFB method in a couple hours.

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Could be a revolution in nonlinear network modeling.