AI Article Synopsis
- The text examines symbolic regression as a new method for analyzing how materials disperse light, contrasting it with traditional approaches.
- Using genetic programming, the authors develop mathematical expressions from experimental optical data for both transparent and absorbing materials.
- Initially focusing on transparent dielectrics, the study later addresses the complexities of absorbing dielectrics, requiring advanced modeling techniques while adhering to causality principles.
Article Abstract
In this contribution we explore the possibilities and limitations of symbolic regression as an alternative to the approaches currently used to characterize the dispersive behavior of a given material. To this end, we make use of genetic programming to retrieve, from either ellipsometric or spectral data, closed-form expressions that model the optical properties of the materials studied. In a first stage we consider transparent dielectrics for our numerical experiments. Next we increase the complexity of the problem and consider absorbing dielectrics, which not only require the use of complex functions to model their dielectric function, but also imply a supplementary constraint imposed by the verification of the causality principle.
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| http://dx.doi.org/10.1364/OE.468110 | DOI Listing |
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