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        <identifier>oai:figshare.com:article/33970843</identifier>
        <datestamp>2026-09-23T05:37:08Z</datestamp>
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          <dc:title>Dataset and Code for Generative AI-enabled discovery and experimental demonstration of ultra-broadband epsilon-near-zero perfect absorbers</dc:title>
          <dc:creator>David Dang (24362096)</dc:creator>
          <dc:subject>Photonics, optoelectronics and optical communications</dc:subject>
          <dc:subject>Deep learning</dc:subject>
          <dc:subject>Machine Learning</dc:subject>
          <dc:subject>Epsilon Near Zero</dc:subject>
          <dc:subject>Generative AI</dc:subject>
          <dc:subject>Absorbers</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;This repository contains the code, trained models, and experimental datasets used for the paper AI-Driven Design and Experimental Demonstration of Ultra-Broadband Epsilon-Near-Zero Perfect Absorbers. The project validates a physics‑informed generative neural network with experimental fabrication and optical measurements of the generated structures.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-23T05:37:08Z</dc:date>
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          <dc:type>Dataset</dc:type>
          <dc:identifier>10.6084/m9.figshare.33970843.v1</dc:identifier>
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