Summary of Data-driven 2d Stationary Quantum Droplets and Wave Propagations in the Amended Gp Equation with Two Potentials Via Deep Neural Networks Learning, by Jin Song et al.
Data-driven 2D stationary quantum droplets and wave propagations in the amended GP equation with two potentials via deep neural networks learning
by Jin Song, Zhenya Yan
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Mathematical Physics (math-ph); Pattern Formation and Solitons (nlin.PS); Computational Physics (physics.comp-ph); Optics (physics.optics)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper develops a systematic deep learning approach to solve 2D stationary quantum droplets (QDs) and investigates their wave propagation in the amended Gross-Pitaevskii equation with Lee-Huang-Yang correction and two kinds of potentials. The initial-value iterative neural network (IINN) algorithm is used for 2D stationary QDs, while physics-informed neural networks (PINNs) are employed to explore their evolutions in a space-time region. The approach is demonstrated on two types of potentials, including the 2D quadruple-well Gaussian potential and the PT-symmetric HO-Gaussian potential, which lead to spontaneous symmetry breaking and the generation of multi-component QDs. This deep learning method has potential applications in studying wave propagations of other nonlinear physical models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computers to solve a problem in physics called quantum droplets. These droplets are like tiny clouds that move around in space. The scientists used special computer programs called neural networks to figure out how these droplets move and change shape. They tested their method on two different types of “potentials” which are like invisible forces that affect the droplets. This research could help us understand more about the behavior of quantum droplets and other physical systems. |
Keywords
» Artificial intelligence » Deep learning » Neural network
