Kolmogorov n-widths for linear dynamical systems
Not on the map. Why?
Abstract (arXiv)
Kolmogorov n-widths and Hankel singular values are two commonly used concepts in model reduction. Here we show that for the special case of linear time-invariant dynamical (LTI) systems, these two concepts are directly connected. More specifically, the greedy search applied to the Hankel operator of an LTI system resembles the minimizing subspace for the Kolmogorov n-width and the Kolmogorov n-width of an LTI system equals its (n+1)st Hankel singular value once the subspaces are appropriately defined. We also establish a lower bound for the Kolmorogov n-width for parametric LTI systems and illustrate that the method of active subspaces can be viewed as the dual concept to the minimizing subspace for the Kolmogorov n-width.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Jun 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2022: 3; 2023: 1; 2024: 1; 2025: 1; 2026: 1.
7 papers.
- 2022Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networkscited by 26
- 2022Predicting waves in fluids with deep neural networkcited by 7
- 2023Low-dimensional Data-based Surrogate Model of a Continuum-mechanical Musculoskeletal System Based on Non-intrusive Model Order Reductioncited by 6
- 2024Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networkscited by 1
- 2025Leveraging time and parameters for nonlinear model reduction methodscited by 1
- 2026Nonlinear model reduction for transport-dominated problemscited by 1
- 2022Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamicscited by 0
- 2026Nonlinear model reduction for transport-dominated problems1 Feb 2026
- 2025Leveraging time and parameters for nonlinear model reduction methods7 Jan 2025
- 2024Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks14 Feb 2024
- 2023Low-dimensional Data-based Surrogate Model of a Continuum-mechanical Musculoskeletal System Based on Non-intrusive Model Order Reduction13 Feb 2023
- 2022Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics1 Nov 2022
- 2022Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks5 May 2022
- 2022Predicting waves in fluids with deep neural network17 Jan 2022