Visualizing a neural network that develops quantum perturbation theory
Name
PhysRevA.98.010701.pdf
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904.56 KB
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Adobe PDF
Checksum (MD5)
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Author(s)
Wu, Yadong
Zhang, Pengfei
Shen, Huitao
Zhai, Hui
Date Issued
July 2018
Journal
Physical Review A
Publisher
American Physical Society
Citation
Wu, Yadong, Pengfei Zhang, Huitao Shen and Hui Zhai. "Visualizing a neural network that develops quantum perturbation theory." Physucial Review A 98 (2018), 010701.
Version
Final published version
Abstract
Motivated by the question whether the empirical fitting of data by neural networks can yield the same structure of physical laws, we apply neural networks to a quantum-mechanical two-body scattering problem with short-range potentials—a problem that by itself plays an important role in many branches of physics. After training, the neural network can accurately predict s-wave scattering length, which governs the low-energy scattering physics. By visualizing the neural network, we show that it develops perturbation theory order by order when the potential depth increases, without solving the Schrödinger equation or obtaining the wave function explicitly. The result provides an important benchmark to the machine-assisted physics research or even automated machine learning physics laws.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1103/PhysRevA.98.010701
