首页|Massachusetts Institute of Technology Researchers Describe Recent Advances in Machine Learning (Adjoint method in machine learning: A pathway to efficient inverse design of photonic devices)
Massachusetts Institute of Technology Researchers Describe Recent Advances in Machine Learning (Adjoint method in machine learning: A pathway to efficient inverse design of photonic devices)
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Data detailed on artificial intelligence have been presented. According to news originating from the Massachusetts Institute of Technology by NewsRx correspondents, research stated, “Innovative machine learning techniques have facilitated the inverse design of photonic structures for numerous practical applications. Nevertheless, the quantity of data and the initial data distribution are paramount for the discovery of highly efficient photonic devices.” Financial supporters for this research include National Research Foundation of Korea; Institute For Infor- 54 mation Communication Technology Planning And Evaluation; Korea Semiconductor Research Consortium; Korea Ministry of Trade Industry And Energy. The news reporters obtained a quote from the research from Massachusetts Institute of Technology: “These devices often require simulated data ranging from thousands to several hundred thousand data points. This issue has consistently posed a major hurdle in machine learning-based photonic design problems. Therefore, we propose a new data augmentation algorithm grounded in the adjoint method, capable of generating more than 300 times the amount of original data while enhancing device efficiency. The adjoint method forecasts changes in the figure of merit (FoM) resulting from structural perturbations, requiring only two full-wave Maxwell simulations for this prediction. By leveraging the adjoint gradient values, we can augment and label several thousand new data points without any additional computations. Furthermore, the augmented data generated by the proposed algorithm displays significantly improved FoMs.”
Massachusetts Institute of TechnologyAlgorithmsCyborgsEmerging TechnologiesMachine Learning
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