Related papers: Machine-learning-assisted material and geometry characterization from Casimir force measurement

Machine-learning-assisted material and geometry characterization from Casimir force measurement

URL: http://arxiv.org/abs/2604.15763v1
Date: Fri, 17 Apr 2026 07:07:20 GMT
Title: Machine-learning-assisted material and geometry characterization from Casimir force measurement
Abstract summary: Quantum vacuum fluctuations provide a broadband electromagnetic source.<n>We show that fluctuations can enable measurement of material permittivity over a broad frequency range.<n>Our work highlights the application potential of using vacuum fluctuations as a naturally-existing broadband electromagnetic source.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: A broadband electromagnetic source is important for scientific and technological applications. Quantum vacuum fluctuations, which manifest most prominently in the Casimir effect, provide a fundamentally broadband electromagnetic source. Here we explore a potential consequence of the broadband nature of quantum vacuum fluctuations, by showing that such fluctuations can enable measurement of material permittivity over a broad frequency range. Specifically, we consider the Casimir force in a parallel-plate geometry, with one plate covered by a nanoscopic thin film. Using a machine learning approach, we show that one can infer both the thickness of the film and its permittivity over a broad frequency range, starting from the dependency of the Casimir forces on the spacing between the two plates. Our work highlights the application potential of using vacuum fluctuations as a naturally-existing broadband electromagnetic source for material characterization, and shows that the inverse problem in Casimir force calculation can be solved with machine learning.

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