Addressing the non-perturbative regime of the quantum anharmonic oscillator by physics-informed neural networks

The use of deep learning in physical sciences has recently boosted the ability of researchers to tackle physical systems where little or no analytical insight is available. Recently, the Physics−Informed Neural Networks (PINNs) have been introduced as one of the most promising tools to solve systems...

詳細記述

書誌詳細

出版年:	New Journal of Physics
主要な著者:	Lorenzo Brevi, Antonio Mandarino, Enrico Prati
フォーマット:	論文
言語:	英語
出版事項:	IOP Publishing 2024-01-01
主題:	physics-informed neural networ quantum anharmonic oscillator deep learning for nonintegrable systems
オンライン･アクセス:	https://doi.org/10.1088/1367-2630/ad8302