Fast active thermal cloaking through PDE-constrained optimization and reduced-order modeling
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Abstract (arXiv)
In this paper we show how to efficiently achieve thermal cloaking from a computational standpoint in several virtual scenarios by controlling a distribution of active heat sources. We frame this problem in the setting of PDE-constrained optimization, where the reference field is the solution of the time-dependent heat equation in the absence of the object to cloak. The optimal control problem then aims at actuating the space-time control field so that the thermal field outside the obstacle is indistinguishable from the reference field. In particular, we consider multiple scenarios where material's thermal diffusivity, source intensity and obstacle's temperature are allowed to vary within a user-defined range. To tackle the thermal cloaking problem in a rapid and reliable way, we rely on a parametrized reduced order model built through the reduced basis method, thus entailing huge computational speedups compared to high-fidelity, full-order model exploiting the finite element method while dealing both with complex target shapes and disconnected control domains.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2021.
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.
- 2023Multi-fidelity reduced-order surrogate modelingcited by 18
- 2024Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order modelscited by 4
- 2024Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order modelscited by 0
- 2024Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models13 Dec 2024
- 2024Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models9 Sep 2024
- 2023Multi-fidelity reduced-order surrogate modeling1 Sep 2023