On the representation of energy-preserving quadratic operators with application to Operator Inference

In this work, we investigate a skew-symmetric parameterization for energy-preserving quadratic operators. Earlier, [Goyal et al. (2023)] proposed this parameterization to enforce energy-preservation of quadratic terms in non-intrusive model reduction. We here prove that every energy-preserving quadratic term can be equivalently formulated using a parameterization of the corresponding operator via skew-symmetric matrix blocks. We develop an algorithm to compute an equivalent quadratic operator with skew-symmetric sub-matrices, given an arbitrary energy-preserving operator. Consequently, we employ the skew-symmetric sub-matrix representation in the framework of non-intrusive reduced-order modeling (ROM) via Operator Inference (OpInf) for systems with an energy-preserving nonlinearity. To this end, we propose a sequential, linear least-squares (LS) problems formulation for the inference task, to ensure energy-preservation of the data-driven quadratic operator. The potential of this approach is indicated by the numerical results for a 1D Korteweg–de Vries and a 2D Burgers’ equation benchmark; the inferred system dynamics are accurate, while the corresponding operators are faithful to the underlying physical properties of the system.