ArticleNanomaterials (Basel, Switzerland)2026
Accurate Inverse Design of Broadband Solar Metamaterial Absorbers via Joint Forward-Inverse Deep Learning.
Article in Nanomaterials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Resonance-Anchored Residual Surrogate for Spectral Prediction and Inverse Design of Multiple Terahertz Metamaterial Absorber Topologies.Nanomaterials (Basel, Switzerland) · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
The design of broadband, high-efficiency solar absorbers remains challenging due to the complex and ill-posed inverse mapping from the target optical responses to the physical structures in inverse design optimization. To address this, we propose a joint forward-inverse deep learning framework that enables the rapid and accurate optimization of multilayer metamaterial absorbers. This method integrates an inverse network based on a Modified Swin Transformer with a Multilayer Perceptron forward proxy and performs end-to-end training in a consistency-driven cycle. This strategy reduces the one-to-many ambiguity in inverse design and improves the prediction accuracy, with normalized test mean squared errors of 7.2 × 10
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Registered trials
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