SOLIS: Physics-Informed Learning of Interpretable Neural Surrogates for Nonlinear Systems
arXiv:2604.14879v1 Announce Type: new
Abstract: Nonlinear system identification must balance physical interpretability with model flexibility. Classical methods yield structured, control-relevant models but rely on rigid parametric forms that often mi…