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pro vyhledávání: '"Ryan Tsai"'
In recent years, hybrid design strategies combining machine learning (ML) with electromagnetic optimization algorithms have emerged as a new paradigm for the inverse design of photonic structures and devices. While a trained, data-driven neural netwo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab6f46d0c426abbc302c663ec279e381
http://arxiv.org/abs/2209.15434
http://arxiv.org/abs/2209.15434
Conditional Machine Learning-Based Inverse Design Across Multiple Classes of Nanophotonic Structures
Autor:
Benjamin Pham, Yusaku Kawagoe, David Ho, Christopher Yeung, Ryan Tsai, Brian King, Mark W. Knight, Aaswath Raman, Julia Liang
Publikováno v:
Conference on Lasers and Electro-Optics.
We present a machine learning-based photonics design strategy centered on encoding image colors with material and structural data. Given input target spectra, our model can accurately determine the optimal metasurface class, materials, and structure.
Autor:
Mark W. Knight, Yusaku Kawagoe, Brian King, Julia Liang, Benjamin Pham, David Ho, Christopher Yeung, Ryan Tsai, Aaswath Raman
Publikováno v:
Advanced Optical Materials. 9:2170079
Autor:
Aaswath Raman, Yusaku Kawagoe, Christopher Yeung, Brian King, Mark W. Knight, Julia Liang, Benjamin Pham, David Ho, Ryan Tsai
Publikováno v:
Advanced Optical Materials. 9:2100548
Understanding how nano- or micro-scale structures and material properties can be optimally configured to attain specific functionalities remains a fundamental challenge. Photonic metasurfaces, for instance, can be spectrally tuned through material ch