Universität zu Köln

Liu, Xiwang, Mang, Guojun, Li, Jie, Shi, Guangluo, Zhou, Mingyang, Huang, Boqiang, Tang, Yajuan, Song, Xiaohong and Yang, Weifeng ORCID: 0000-0001-7451-9081 (2020). Deep Learning for Feynman's Path Integral in Strong-Field Time-Dependent Dynamics. Phys. Rev. Lett., 124 (11). COLLEGE PK: AMER PHYSICAL SOC. ISSN 1079-7114

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Abstract

Feynman's path integral approach is to sum over all possible spatiotemporal paths to reproduce the quantum wave function and the corresponding time evolution, which has enormous potential to reveal quantum processes in the classical view. However, the complete characterization of the quantum wave function with infinite paths is a formidable challenge, which greatly limits the application potential, especially in the strong-field physics and attosecond science. Instead of brute-force tracking every path one by one, here we propose a deep-learning-performed strong-field Feynman's formulation with a preclassification scheme that can predict directly the final results only with data of initial conditions, so as to attack unsurmountable tasks by existing strong-field methods and explore new physics. Our results build a bridge between deep learning and strong-field physics through Feynman's path integral, which would boost applications of deep learning to study the ultrafast time-dependent dynamics in strong-field physics and attosecond science and shed new light on the quantum-classical correspondence.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Liu, Xiwang UNSPECIFIED UNSPECIFIED UNSPECIFIED Mang, Guojun UNSPECIFIED UNSPECIFIED UNSPECIFIED Li, Jie UNSPECIFIED UNSPECIFIED UNSPECIFIED Shi, Guangluo UNSPECIFIED UNSPECIFIED UNSPECIFIED Zhou, Mingyang UNSPECIFIED UNSPECIFIED UNSPECIFIED Huang, Boqiang UNSPECIFIED UNSPECIFIED UNSPECIFIED Tang, Yajuan UNSPECIFIED UNSPECIFIED UNSPECIFIED Song, Xiaohong UNSPECIFIED UNSPECIFIED UNSPECIFIED Yang, Weifeng UNSPECIFIED orcid.org/0000-0001-7451-9081 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-340573
DOI:	10.1103/PhysRevLett.124.113202
Journal or Publication Title:	Phys. Rev. Lett.
Volume:	124
Number:	11
Date:	2020
Publisher:	AMER PHYSICAL SOC
Place of Publication:	COLLEGE PK
ISSN:	1079-7114
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language NEURAL-NETWORKS; IONIZATION; TRANSITIONS; HOLOGRAPHY; GAME; GO Multiple languages Physics, Multidisciplinary Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/34057