Efficient space-time reduced order model for linear dynamical systems in Python using less than 120 lines of code

Abstract

A classical reduced order model (ROM) for dynamical problems typically involves only the spatial reduction of a given problem. Recently, a novel space-time ROM for linear dynamical problems has been developed, which further reduces the problem size by introducing a temporal reduction in addition to a spatial reduction without much loss in accuracy. The authors show an order of a thousand speed-up with a relative error of less than 0.00001 for a large-scale Boltzmann transport problem. In this work, we present for the first time the derivation of the space-time Petrov-Galerkin projection for linear dynamical systems and its corresponding block structures. Utilizing these block structures, we demonstrate the ease of construction of the space-time ROM method with two model problems: 2D diffusion and 2D convection diffusion, with and without a linear source term. For each problem, we demonstrate the entire process of generating the full order model (FOM) data, constructing the space-time ROM, and predicting the reduced-order solutions, all in less than 120 lines of Python code. We compare our Petrov-Galerkin method with the traditional Galerkin method and show that the space-time ROMs can achieve O(100) speed-ups with O(0.001) to O(0.0001) relative errors for these problems. Finally, we present an error analysis for the space-time Petrov-Galerkin projection and derive an error bound, which shows an improvement compared to traditional spatial Galerkin ROM methods.

24 pages, 18 figures

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• gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification
• GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder
• A fast and accurate domain-decomposition nonlinear manifold reduced order model
• Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning
• Gappy AE: A Nonlinear Approach for Gappy Data Reconstruction using Auto-Encoder
• Linear model reduction using spectral proper orthogonal decomposition
• Higher-Order LaSDI: Reduced Order Modeling with Multiple Time Derivatives