Chinese︱Feedback︱Save this page

Login︱Register

• Elaborating Academic Views 　　　　 • Exchanging Innovative Ideas
• Protecting Intellectual Properties 　　• Fast Sharing Science Papers

Sponsored by the Center for Science and Technology Development of the Ministry of Education
Supervised by Ministry of Education of the People's Republic of China

Home > Papers > Physics

There are 916 papers published in subject: since this site started.

Results per page:

916 Total, 92 Pages | 1 2 3 4 Next > Last >>

Select Subject

All (8828)

Agronomy (158)

Animal Husbandry,... (64)

Aquatic Science (16)

Astronomy (36)

Aviation (22)

Basic Medicine (184)

Biology (504)

Chemical Engineering (103)

Chemistry (635)

Civil and Archite... (105)

Clinical Medicine (236)

Computer Science ... (924)

Dynamic and Elect... (121)

Earth Science (119)

Economics (67)

Education (31)

Electrics, Commun... (927)

Energy Science an... (67)

Engineering and T... (15)

Environmental Sci... (140)

Food Science and ... (63)

Forestry (36)

Hydraulic Enginee... (40)

Mechanics (240)

Metallurgical Eng... (32)

Mine Engineering ... (38)

Nuclear Science a... (21)

Pharmacy (98)

Physics (920)

Preventive Medici... (37)

Psychology (20)

Security Science ... (17)

Sports Science (6)

Surveying and Map... (15)

Textile Science a... (16)

Traditional Chine... (56)

Transportation En... (57)

Second-level Subject

Acoustics(23)
Applied Physics(9)
Atom Molecule Physics(51)
Computational Physics(10)
Condensed Matter Physics(316)
Electromagnetism(9)
Electron Physics(12)
High-energy Physics(32)
History of Physics(1)
Nuclear Physics(20)
Optics(242)
Other Subjects of Physics(5)
Plasma Physics(14)
Radio Physics(11)
Theoretical Physics(154)
Thermology(11)

Rank

Results per page:

916 Total, 92 Pages | 1 2 3 4 Next > Last >>

Alert Name:
Alerting to:
Authentication email will be sent to your email address in 24 hours
Frequency:
Email Message Format:	Plain text Graphical(HTML)

Complete the form below and we will recommend the selected titles to your friends on your behalf. * Indicates a required field.
Your name*:
Your email address*:
Recipient's name*:
Recipient's email address*:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find the page(s) useful.

Your name:
Your email address:
Recipient's name:
Recipient's email address:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find this page useful.

Disclaimer: This message was sent to your friend using the "Send it to a friend" facility on the Sciencepaper Online’ WWW site, http://www.paper.edu.cn/en. The Sciencepaper Online is not responsible for the content of this email, and anything said in this email does not necessarily reflect the Sciencepaper Online's views.


	1. Evolution of magnetization textures in Mn1.4PtSn under magnetic field
	FU Peng,DING Bei,WANG Wenhong,YAO Yuan,LANG Peilin
	Physics 01 April 2024
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:In this study, we used Lorentz transmission electron microscopy (LTEM) and the transport of intensity equation (TIE) method to investigate the spin structures in the Mn1.4PtSn magnet, which exhibits D2d symmetry. Our goal was to observe how these spin structures evolve under the influence of an external magnetic field. In particular, the application of a perpendicular field was able to modulate the stripe domain widths and induce the formation of anti-skyrmions. While an in-plane magnetic field can control the state of these anti-skyrmions, the coexistence of different skyrmions suggests that the hysteresis properties of the magnet may affect the effectiveness of the field control.
	TO cite this article:FU Peng,DING Bei,WANG Wenhong, et al. Evolution of magnetization textures in Mn1.4PtSn under magnetic field[OL].[ 1 April 2024] http://en.paper.edu.cn/en_releasepaper/content/4762933


	2. Scaling behavior in the asymmetric quantum Rabi model
	QING Yu-Qi, LIU Mao-Xin
	Physics 21 March 2024
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:The scaling behavior and critical exponents are examined in the asymmetric quantum Rabi model, where the parity symmetry is violated. A phase transition occurs in this model, allowing for the determination of critical exponents. A general scaling function is formulated, encompassing two scaling variables. Additionally, a quantum hyper-scaling relation between critical exponents is established. The investigation reveals that the scaling form remains applicable, employing an alternative scaling variable, even when the coupling is below the critical point. This discovery offers valuable insights into the experimental investigation of the super-radiant phase transition, particularly in the cavity platform, which has been limited by the well-known no-go theorem.
	TO cite this article:QING Yu-Qi, LIU Mao-Xin. Scaling behavior in the asymmetric quantum Rabi model[OL].[21 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4763013

Saved Papers

Saved Papers


	3. Subtle dynamics of chaotic torsion pendulum: a detailed comparison between experiments and numerical simulations
	Xie Gui-Jin, GAO Jian, XIAO Jing-Hua, YAN Zi-Xiang
	Physics 13 March 2024
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:We conduct a detailedexperimental and numerical study on the subtle dynamics of chaotic torsion pendulum (CTP).We first present experimental observations reported by students, and then propose a revised model of CTP based on laws of mechanics and insights about the experiment to understand these observations.Parameters of the revised model are fit using experimental data.The revised model agrees well with experimental observations.The subtle dynamics hidden in these phenomena are thoroughly exhibited through this study, hoping to provide more insights to the nonlinear nature of CTP.
	TO cite this article:Xie Gui-Jin, GAO Jian, XIAO Jing-Hua, et al. Subtle dynamics of chaotic torsion pendulum: a detailed comparison between experiments and numerical simulations[OL].[13 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762737


	4. Error Stairs of Reservoir Computing and its Applications
	JIA Lin-Yuan, GAO Jian, YAN Zi-Xiang, ZHAO Hui, ZHAO Hui, XIAO Jing-Hua
	Physics 13 March 2024
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:In this paper we explore with learning capacity of reservoir computing with polynomial functions, and find a universal error stairs, which does not depend on the input signals, and can effectively describe the learning capability of the reservoir. Machine learning methods based on reservoir computing have shown superior performance in predicting the dynamics of complex systems. However, the generation process of the reservoir is often considered a `black box', so it is of great significance to characterize the learning capability of the reservoir. Based on the error stairs, we propose two indicators to characterize the learning capability of the reservoir, the highest order polynomial error and memory length, which describe the nonlinear processing ability and memory ability of the reservoir respectively. We validate and apply these two indicators through predictions on classical chaotic systems such as the Logistic map and the Lorenz system. These two indicators, for nonlinear processing ability and memory capacity respectively, provide a promising tool to study the learning capacity of reservoir computing and other machine learning method for dynamical systems.
	TO cite this article:JIA Lin-Yuan, GAO Jian, YAN Zi-Xiang, et al. Error Stairs of Reservoir Computing and its Applications[OL].[13 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762711


	5. Large sampling intervals for learning and predicting chaotic systems with reservoir computing
	XIE Qing-yan, YAN Zi-Xiang, GAO Jian, ZHAO Hui, ZHAO Hui, XIAO Jing-Hua
	Physics 13 March 2024
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Reservoir computing is an efficient artificial neural network with low training cost and low hardware overhead. It is widely used in time sequence information processing, such as waveform classification, speech recognition, time series prediction, etc. However, in practical applications, researchers can only use limited information from the system for predictions, and the sampling interval cannot be adjusted freely due to the limitations of the actual system. Based on the above situation, we demonstrate the impact of time and space sampling intervals on the short-term and long-term prediction capabilities of the reservoir computing and compare it with the existing numerical methods. It can be found that for chaotic systems, the reservoir computing can learn and reproduce the systems' states at almost five times larger spatio-temporal intervals compared to classical numerical methods, such as fourth-order Runge-Kutta and spectral methods. Our results show the captivity of reservoir computing in the applications with limitation of spatio-temporal intervals, and pave the way to reservoir-based fast numerical simulation methods.
	TO cite this article:XIE Qing-yan, YAN Zi-Xiang, GAO Jian, et al. Large sampling intervals for learning and predicting chaotic systems with reservoir computing[OL].[13 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762703


	6. Quantum Transport Simulations of α-In2Se3 Antiferroelectric Tunnel Junctions
	Zhang Lingxue,Zhang Jiaxin,Sun Yuxuan,Li Wei,Quhe Ruge
	Physics 08 March 2024
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Due to semiconductor characteristics and non-volatile ferroelectricity, two-dimensional (2D) In2Se3 are considered as potential candidates for next-generation storage and computing devices. Based on first principles calculations, we designed antiferroelectric tunnel junctions (AFTJs) using α-In2Se3 as channels. The tunneling barrier height is controlled by the antiferroelectric to ferroelectric (AFE-FE) phase transition of the channel. A maximum current ratio up to 426 is predicted between the AFE and FE phases, enabling the two distinct memory states. By constructing two AFTJs into a calculation unit, the total current can either be fully turned on/off or function as XNOR logic with bias as inputs. Our research provides a new approach to implementing integrated storage and computing devices, making it possible for efficient data centric applications in the era of big data.
	TO cite this article:Zhang Lingxue,Zhang Jiaxin,Sun Yuxuan, et al. Quantum Transport Simulations of α-In2Se3 Antiferroelectric Tunnel Junctions[OL].[ 8 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762525


	7. Resistance switching characteristic of Ag/Fe2O3/MoS2/Ag with very low switching voltage
	SHU Haiyan,HE Chaotao,ZHANG Xingwen,LI Shichang,CHEN Peng
	Physics 28 November 2023
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:In this paper,the resistive switching characteristics of Ag/Fe2O3/MoS2/Ag multilayer film deposited on ITO by magnetron sputtering are investigated.The Ag/Fe2O3/MoS2/Ag device exhibits superior resistive switching behavior compared to the device without Fe2O3 layer due to the positive effect of oxygen vacancies in Fe2O3 on the formation of conducting filaments. The resistive switching ratio of the device is close to 7.0 × 105. The current value of the device drops sharply at 0.12 V when the voltage is swept forward, and the device switches from HRS back to LRS at -0.28 V when a voltage of opposite polarity is applied.The I-V curves of the device are fitted in double logarithmic coordinates, and it is found that the device is controlled by an ohmic conduction model in the low resistance state and two conduction models in the high resistance state: in the low bias region, which exhibits ohmic conduction, and at higher voltages, which is controlled by the SCLC conduction model. Such a resistive switching characteristic with very low switching voltage and high resistance ratio is of particular importance in the application of resistive stochastic storage.
	TO cite this article:SHU Haiyan,HE Chaotao,ZHANG Xingwen, et al. Resistance switching characteristic of Ag/Fe2O3/MoS2/Ag with very low switching voltage[OL].[28 November 2023] http://en.paper.edu.cn/en_releasepaper/content/4761573


	8. Physics-informed Neural Network method for predicting soliton dynamics supported by complex PT-symmetric potentials
	LIU Ximeng,ZHANG Zhiyang,LIU Wenjun
	Physics 06 May 2023
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:We examine the deep learning technique referred to as the physics-informed neural network method for approximating nonlinear Schr?dinger equation under considered parity time symmetric potentials and obtaining multifarious soliton solutions. For the first time, neural networks founded principally physical information are adopted to figure out the solution the examined nonlinear partial differential equation and generate six different types of soliton solutions, which are basic, dipole, tripole, quadruple, pentapole and sextupole solitons we consider. We make comparisons between the predicted and actual soliton solutions to see whether deep learning is capable of seeking the solution the partial differential equation described before. We may assess whether physics-informed neural network is capable of effectively providing approximate soliton solutions through the evaluation of squared error between the predicted and numerical results. Besides, we also scrutinize how different activation mechanisms and network architectures impact the capability of selected deep learning technique works.Through the findings we can prove that the neural networks model we established can be utilized to accurately and effectively approximate nonlinear Schr?dinger equation under consideration and predict the dynamics of soliton solution.
	TO cite this article:LIU Ximeng,ZHANG Zhiyang,LIU Wenjun. Physics-informed Neural Network method for predicting soliton dynamics supported by complex PT-symmetric potentials[OL].[ 6 May 2023] http://en.paper.edu.cn/en_releasepaper/content/4760642


	9. Noise Resistance of Next Generation Reservoir Computing: A Comparative Study with High-Order Correlation Computation
	LIU Sheng-Yu,XIAO Jing-Hua,YAN Zi-Xiang,GAO Jian
	Physics 01 April 2023
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:Reservoir computing (RC) methods have received more and more attention and applications in chaotic time series prediction with their simple structure and training method. Recently, the next generation reservoir computing (NG-RC) method (Nature Communications,12,5564) has been proposed with less training cost and better time-series predictions. Nevertheless, in practice, available data on dynamic systems are contaminated with noise. Though NG-RC is shown highly efficient in learning and predicting, its noise resistance captivity is not clear yet, limiting its use in practical problems. In this paper, we study the noise resistance of the NG-RC method, taking the well-known denoising method, the high-order correlation computation (HOCC) method, as a reference. Both methods have similar procedures in respect of function bases and regression processes. With the simple ridge regression method, the NG-RC method has a strong noise resistance for white noise, even better than the HOCC method. Besides, the NG-RC method also shows a good prediction ability for small color noise, while it does not provide correct reconstruct dynamics. In this paper, other than reconstruction parameters, four numerical indicators are used to check the noise resistance comprehensively, such as the training error, prediction error, prediction time, and auto-correlation prediction error, for both the short-time series and long climate predictions. Our results provide a systematic estimation of NG-RC's noise resistance capacity, which is helpful for its applications in practical problems.
	TO cite this article:LIU Sheng-Yu,XIAO Jing-Hua,YAN Zi-Xiang, et al. Noise Resistance of Next Generation Reservoir Computing: A Comparative Study with High-Order Correlation Computation[OL].[ 1 April 2023] http://en.paper.edu.cn/en_releasepaper/content/4759954


	10. Quantum Transport Simulations of a Proposed Logic In-Memory Device Based on Bipolar Magnetic Semiconductor
	Ke Yunzhe,Yin Guoxue,Zhang Lingxue,Li Wei,Quhe Ruge
	Physics 20 March 2023
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (0 B)

	Abstract:To overcome the memory wall based on the von Neumann architecture, in-memory computing has been intensively studied as a potential solution. Recently, a new type of spintronic material, namely bipolar magnetic semiconductors (BMSs), draw much attention because of its opposite spin-polarized valence and conduction bands and thus facilitates electrically tunable spin transport. Here, we propose a novel logic-in-memory device with a traditional field effect transistor (FET) configuration by making use of the ferromagnetic and semiconducting features of BMSs simultaneously. Two represented BMSs (2H-VS2 and semihydrogenated graphene) are selected as the channel of FETs and the transport properties of these devices have been investigated by using ab initio quantum transport simulations. The spin polarization of the current reaches up to 98%, enabling the device to provide an ideal spin polarization signal. The distinct electronic structures under the two magnetic states and the electrically tunable spin polarization allow the devices to perform logic operations directly in situ. Two-input NAND and OR logic and non-volatile NOR logic gates can be realized with one and two BMS FETs, respectively, efficiently decreasing the integration density of logical circuits. This work provides a new route to realize fused storage and computing functions in a single transistor.
	TO cite this article:Ke Yunzhe,Yin Guoxue,Zhang Lingxue, et al. Quantum Transport Simulations of a Proposed Logic In-Memory Device Based on Bipolar Magnetic Semiconductor[OL].[20 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759823

	Check out RSS, or use RSS reader to subscribe this item