A Dynamic Subspace Approach for Low-rank Approximation of Large-scale Nonlinear Systems
For researchers in reduced-order modeling of complex dynamical systems, this method offers a scalable way to handle transport-dominated problems that typically require high-rank approximations.
The paper introduces a dynamic subspace method that learns time-continuous trajectories on the Grassmannian manifold to approximate large-scale nonlinear systems, breaking the Kolmogorov barrier in transport-dominated phenomena. It achieves higher accuracy than static low-rank approximations at equivalent ranks, as demonstrated on a 1D transport equation and a turbulent airfoil wake.
We present a dynamic subspace approach for efficiently approximating large-scale systems by learning time-continuous trajectories on the Grassmannian manifold. By parameterizing a low-dimensional basis as a geodesic path, the method allows for adaptive tracking of evolving physics. Our approach decouples the geometric drift of the subspace from the intrinsic state evolution. This avoids the typical rank inflation required by static low-dimensional approximation methods to maintain accuracy, effectively breaking the Kolmogorov barrier in transport-dominated phenomena. To ensure scalability for high-dimensional data, the optimization is performed in a reduced feature space, rendering the computational cost independent of the large original state dimension. Numerical results for a 1D transport equation and a large-scale turbulent airfoil wake demonstrate that this dynamic subspace approach achieves higher accuracy than static linear approximations at equivalent ranks, positioning it as a robust and scalable method for the low-rank modeling of complex, non-stationary dynamical systems.