The Frequency Reduced-Basis method: Reduced order models for time-dependent problems using the Laplace transform
Ricardo Reyes

TL;DR
This paper introduces a novel reduced basis method for time-dependent PDEs using the Laplace transform, enabling efficient and accurate solutions by combining spectral sampling and POD techniques.
Contribution
The method uniquely applies Laplace transform sampling and POD to create exponentially accurate reduced models for time-dependent problems, improving computational efficiency.
Findings
Achieves exponential accuracy in reduced basis construction.
Provides significant speed-up in solving time-dependent PDEs.
Demonstrates effectiveness across various evolution equations.
Abstract
We propose a reduced basis method to solve time-dependent partial differential equations based on the Laplace transform. Unlike traditional approaches, we start by applying said transform to the evolution problem, yielding a time-independent boundary value problem that depends on the complex Laplace parameter. First, in an offline stage, we appropriately sample the Laplace parameter and solve the collection of problems using the finite element method. Next, we apply a Proper Orthogonal Decomposition (POD) to this collection of solutions in order to obtain a reduced basis that is of dimension much smaller than that of the original solution space. This reduced basis, in turn, is then used to solve the evolution problem using any suitable time-stepping method. A key insight to justify the formulation of the method resorts to Hardy spaces of analytic functions. By applying the widely-known…
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Taxonomy
TopicsModel Reduction and Neural Networks
