Reduced-order modeling and system identification of nonlinear dynamics through a varational approach

We present a data-driven, non-intrusive framework with embedded uncertainty quantification to build interpretable reduced-order models (ROMs) using variational autoencoders and variational identification of nonlinear dynamics.