Abstract We present a fast method for nonlinear data-driven model reduction of dynamical systems onto their slowest nonresonant spectral submanifolds (SSMs). While the recently proposed reduced-order modeling SSMLearn uses implicit optimization to fit submanifold data and reduce dynamics normal form, here, we reformulate these tasks as explicit problems under certain simplifying assumptions. In...