1 Institute of Theoretical Physics, School of Physics and Optoelectronic Engineering, Ludong University, Yantai 264025, China; 2 Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
Abstract The diffusion trajectory of a Brownian particle passing over the saddle point of a two-dimensional quadratic potential energy surface is tracked in detail according to the deep learning strategies. Generative adversarial networks (GANs) emanating in the category of machine learning (ML) frameworks are used to generate and assess the rationality of the data. While their optimization is based on the long short-term memory (LSTM) strategies. In addition to drawing a heat map, the optimal path of two-dimensional (2D) diffusion is simultaneously demonstrated in a stereoscopic space. The results of our simulation are completely consistent with the previous theoretical predictions.
(Computer systems: hardware, operating systems, computer languages, and utilities)
Fund: This work was supported by the Natural Science Foundation of Shandong Province (Grant No. ZR2020MA092) and the Innovation Project for Graduate Students of Ludong University (Grant No. IPGS2024-048).
Corresponding Authors:
Chunyang Wang
E-mail: wchy@foxmail.com
Cite this article:
Zengxuan Zhao(赵曾轩), Xiuying Zhang(张秀颖), Pengchen Zhao(赵鹏琛), Chunyang Wang(王春阳), Chunlei Xia(夏春雷), Mushtaq Rana Imran, and Joelous Malamula Nyasulu Trajectory tracking on the optimal path of two-dimensional quadratic barrier escaping 2025 Chin. Phys. B 34 050205
[1] Aguiar C E, Barbosa V C, Donangelo R and Souza S R 1989 Nucl. Phys. A 491 301 [2] Aguiar C E, Barbosa V C and Donangelo R 1990 Nucl. Phys. A 517 205 [3] Abe Y, Boilley D, Giraud B G and Wada T 2000 Phys. Rev. E 61 1125 [4] Zagrebaev V and Greiner W 2005 J. Phys. G: Nucl. Part. Phys. 31 825 [5] Zagrebaev V and Greiner W 2007 J. Phys. G: Nucl. Part. Phys. 34 1 [6] Wang C Y, Jia Y and Bao J D 2008 Phys. Rev. C 77 024603 [7] Wang J, Wang C, Xiao L, Ma H, et al. 2022 Physica A 588 126572 [8] LeCun Y, Bengio Y and Hinton G 2015 Nature 521 436 [9] Wang X, Zhao Y and Pourpanah F 2020 Int. J. Mach. Learn. Cyb. 11 747 [10] Sarker I H 2021 SN Comput. Sci. 2 420 [11] Aggarwal A, Mittal M and Battineni G 2021 Int. J. Inf. Manag. Data Insights 1 100004 [12] Goodfellow I J, Abadie J P,Mirza M, Xu B, et al. 2014 NeurIPS 2 2672 [13] Hochreiter S and Schmidhuber J 1997 Neural Comput. 9 1735 [14] Lindemann B, Mller T, Vietz H, Jazdi N and Weyrich M 2021 Procedia CIRP 99 650 [15] Schmidt R M 2019 arxiv:1912.05911 [16] Schmidhuber J 2015 Neural Networks 61 85 [17] Zhang T B, Liang H J, Wang S G, et al. 2022 Chin. Phys. B 31 080701 [18] Zhan Y and Shizgal B D 2019 Phys. Rev. E 99 042101 [19] Basak S, Sengupta S and Chattopadhyay K 2019 Biophys. Rev. 11 851 [20] Berezhkovskii AMand Makarov D E 2018 J. Chem. Phys. 148 201102 [21] Li W and Ma A 2016 J. Chem. Phys. 144 114103 [22] Aquilanti V, Coutinho N D and C-Silva V H 2017 Philos. T. R. Soc. A 375 20160201 [23] Sato Y and Klages R 2019 Phys. Rev. Lett. 122 174101 [24] Li T, Cheng K, Peng Z, et al. 2023 Chin. Phys. B 32 104501
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