  Data-driven model reduction methods provide a nonintrusive way of
constructing computationally efficient surrogates of high-fidelity models for
real-time control of soft robots. This work leverages the Lagrangian nature of
the model equations to derive structure-preserving linear reduced-order models
via Lagrangian Operator Inference and compares their performance with prominent
linear model reduction techniques through an anguilliform swimming soft robot
model example with 231,336 degrees of freedom. The case studies demonstrate
that preserving the underlying Lagrangian structure leads to learned models
with higher predictive accuracy and robustness to unseen inputs.
