Midtvedt Daniel, Mylnikov Vasilii, Stilgoe Alexander, Käll Mikael, Rubinsztein-Dunlop Halina, Volpe Giovanni
Department of Physics, University of Gothenburg, Gothenburg, Sweden.
Department of Physics, Chalmers University of Technology, Gothenburg, Sweden.
Nanophotonics. 2022 Jun 14;11(14):3189-3214. doi: 10.1515/nanoph-2022-0197. eCollection 2022 Jul.
The deep-learning revolution is providing enticing new opportunities to manipulate and harness light at all scales. By building models of light-matter interactions from large experimental or simulated datasets, deep learning has already improved the design of nanophotonic devices and the acquisition and analysis of experimental data, even in situations where the underlying theory is not sufficiently established or too complex to be of practical use. Beyond these early success stories, deep learning also poses several challenges. Most importantly, deep learning works as a black box, making it difficult to understand and interpret its results and reliability, especially when training on incomplete datasets or dealing with data generated by adversarial approaches. Here, after an overview of how deep learning is currently employed in photonics, we discuss the emerging opportunities and challenges, shining light on how deep learning advances photonics.
深度学习革命正在为在各个尺度上操纵和利用光提供诱人的新机会。通过从大型实验或模拟数据集中构建光与物质相互作用的模型，深度学习已经改进了纳米光子器件的设计以及实验数据的采集和分析，即使在基础理论尚未充分确立或过于复杂而无法实际应用的情况下也是如此。除了这些早期的成功案例，深度学习也带来了一些挑战。最重要的是，深度学习就像一个黑匣子，难以理解和解释其结果及可靠性，尤其是在不完全数据集上进行训练或处理由对抗性方法生成的数据时。在此，在概述深度学习目前在光子学中的应用方式之后，我们将讨论新出现的机遇和挑战，阐明深度学习如何推动光子学发展。
