New research uses asymptotic analysis to guide AI for resonance prediction

A new research paper introduces an "asymptotics-guided learning" approach for predicting resonances in dispersive media. This method uses asymptotic analysis not just for baseline approximations but also to inform the construction of predictive correction models. By learning the residual between asymptotic and reference resonances with features derived from subwavelength expansion, the approach significantly enhances predictions for single resonators and dimers. The study demonstrates that this technique can yield accurate, low-dimensional, and interpretable formulas for data-driven corrections. AI

IMPACT This research could lead to more accurate and interpretable models for predicting physical phenomena, potentially impacting fields that rely on resonance analysis.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• Asymptotics-guided learning and symbolic regression for dispersive resonances
• Konstantinos Alexopoulos

• paper
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