  Domain wall (DW) devices have garnered recent interest for diverse
applications including memory, logic, and neuromorphic primitives; fast,
accurate device models are therefore imperative for large-scale system design
and verification. Extant DW motion models are sub-optimal for large-scale
system design either over-consuming compute resources with physics-heavy
equations or oversimplifying the physics, drastically reducing model accuracy.
We propose a DW model inspired by the phenomenological similarities between
motions of a DW and a classical object being acted on by forces like air
resistance or static friction. Our proposed phenomenological model predicts DW
motion within 1.2% on average compared with micromagnetic simulations that are
400 times slower. Additionally our model is seven times faster than extant
collective coordinate models and 14 times more accurate than extant
hyper-reduced models making it an essential tool for large-scale DW circuit
design and simulation. The model is publicly posted along with scripts that
automatically extract model parameters from user-provided simulation or
experimental data to extend the model to alternative micromagnetic parameters.
