Abstract:Overcoming the Kolmogorov barrier for constructing efficient reduced-order models (ROMs) for transport-dominated problems remains a challenge. This has impeded their use as computationally cheap surrogates for partial differential equations (PDEs) associated with optimal control problems. Since such problems require multiple computations of the full PDE, employing their reduced-order surrogates instead could speed up the overall optimal control problem. Motivated by this idea, in this paper we explore the use of a nonlinear model-order reduction technique, namely, the shifted proper orthogonal decomposition (sPOD) in an optimal control context. In doing so, we explore the framework of first-optimize-then-reduce (FOTR) where an optimality system for the full PDE problem is constructed first, followed by approximating the optimality system with reduced-order models. We consider this framework for a linear quadratic optimal control problem constrained by a 1D linear advection equation and compare the computational performance of the sPOD method against the use of the standard POD.
From: Shubhaditya Burela [view email]
[v1]
Mon, 1 Jun 2026 10:53:46 UTC (351 KB)
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