The transformational dive of nanophotonics inverse design from deep learning to artificial general intelligence
Year: 2024
Type: article
Abstract: The swift development of artificial intelligence (AI) is significantly transforming the paradigm of nanophotonics. Leveraging universal approximation abilities, AI models sidestep time-consuming electromagnetic simulations, opening the inverse design of photonics systems with millions of design features while offering ample stability and practical scalability compared to traditional optimization methods. This perspective discusses inverse design paradigms enabled by recent advances in AI models, discussing their roles, challenges, and opportunities envisioned by the approaching era of artificial general intelligence.
Source: APL Photonics
DOI: 10.1063/5.0226592
Authors: Qizhou Wang, Yushu Zhang, Arturo Burguete-Lopez, Sergei Rodionov, Andrea Fratalocchi
Institution: King Abdullah University of Science and Technology
Language: English
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,750
OpenAlex ID: W4403431594