Interpreting neural operators: how nonlinear waves propagate in
non-reciprocal solids
Interpreting neural operators: how nonlinear waves propagate in
non-reciprocal solids
We present a data-driven pipeline for model building that combines interpretable machine learning, hydrodynamic theories, and microscopic models. The goal is to uncover the underlying processes governing nonlinear dynamics experiments. We exemplify our method with data from microfluidic experiments where crystals of streaming droplets support the propagation of nonlinear waves …