A5015504840- Theoretical and Computational Physics
- Complex Network Analysis Techniques
- Seismic Imaging and Inversion Techniques
- Complex Systems and Time Series Analysis
- Quantum Information and Cryptography
- Cold Atom Physics and Bose-Einstein Condensates
- Strong Light-Matter Interactions
- Granular flow and fluidized beds
- Landslides and related hazards
- Stochastic processes and statistical mechanics
- Surface and Thin Film Phenomena
- Seismic Waves and Analysis
- Quantum many-body systems
- Advanced Chemical Physics Studies
- Advanced Thermodynamics and Statistical Mechanics
- Physics of Superconductivity and Magnetism
- Geophysical Methods and Applications
- Quantum and electron transport phenomena
- Statistical Mechanics and Entropy
- Underwater Acoustics Research
- Material Dynamics and Properties
- Thermal properties of materials
- Hydrological Forecasting Using AI
- Fluid Dynamics and Heat Transfer
- Neural Networks and Applications
Kunming University of Science and Technology
2024
Beijing Computational Science Research Center
2021-2023
Beijing Normal University
2022-2023
Beijing Haidian Hospital
2023
Chinese Academy of Sciences
2018-2019
Institute of Theoretical Physics
2018-2019
University of Chinese Academy of Sciences
2018
Institut des Sciences de la Terre
2011-2012
Université Gustave Eiffel
2011-2012
Institut Universitaire de France
2012
Abstract Emergence refers to the existence or formation of collective behaviors in complex systems. Here, we develop a theoretical framework based on eigen microstate theory analyze emerging phenomena and dynamic evolution system. In this framework, statistical ensemble composed M microstates system with N agents is defined by normalized × matrix A , whose columns represent order row consist time. The can be decomposed as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"...
We employ the eigen microstates approach to explore self-organized criticality (SOC) in two celebrated sandpile models, namely BTW model and Manna model. In both phase transitions from absorbing state critical can be understood by emergence of dominant with significantly increased weights. Spatial avalanches uniformly characterized a linear system size rescaling. The first temporal reveal scaling relations models. Furthermore, finite-size analysis microstates, we numerically estimate...
Abstract We propose an eigen microstate approach (EMA) for analyzing quantum phase transitions in many-body systems, introducing a novel framework that does not require prior knowledge of order parameter. Using the transverse-field Ising model (TFIM) as case study, we demonstrate effectiveness EMA by identifying key features transition through scaling behavior eigenvalues and structure associated microstates. Our results reveal substantial changes ground state TFIM it undergoes transition,...
The ${\ensuremath{\varphi}}^{4}$ model for structural phase transitions in two dimensions is studied using the Migdal renormalization-group method and a Monte Carlo coarse-graining calculation. methods employed are useful more general models of surface reconstructive transitions. Particular emphasis placed on accuracy diagrams nature "crossover" from order to disorder behavior above (in temperature) but close transition displacive at higher temperatures. calculation found give fullest...
We propose a renormalization group (RG) theory of eigen microstates, which are introduced in the statistical ensemble composed microstates obtained from experiments or computer simulations. A microstate can be considered as linear superposition with probability amplitudes equal to their eigenvalues. Under factor b , largest eigenvalue σ 1 has two trivial fixed points at low and high temperature limits critical point RG relation <?CDATA ${\sigma }_{1}^{b}={b}^{\beta /\nu }{\sigma }_{1}$?>...
We present an overview of the SEISCOPE project on frequency-domain full waveform inversion (FWI). The two main objectives are reconstruction multiple classes parameters and 3D acoustic elastic FWI. optimization relies a reconditioned L-BFGS algorithm which provided scaled gradients misfit function for each parameter. For onshore applications where body waves surface jointly inverted, P- S-wave velocities (VP VS) must be reconstructed simultaneously using hierarchical with nested levels data...
We systematically investigate the excited-state quantum phase transition (ESQPT) in anisotropic Rabi model, which interpolates between model with ${\mathbb{Z}}_{2}$ symmetry and Jaynes-Cummings $\mathbb{U}(1)$ symmetry. calculate energy spectra density of states (DOS) cumulants both analytical numerical ways to describe ESQPT by singularities. In limit, its continuous presents different nonanalytic behaviors from discrete symmetry, there exists a finite discontinuous jump at critical energy....
We present a massively parallel algorithm for 3D acoustic full waveform inversion together with an application to OBC data from the Valhall field. To achieve computational efficiency and flexible algorithm, we design process, which can combine various forward modelling engines (such as finite-difference or finite-element methods) in time frequency domains core formulated domain. Moreover our contains two nested levels of parallelism : source distribution domain decomposition are implemented...
We study the ground state and low-lying polaritonic excitation states in Ising-Rabi lattice model (IRLM), where transverse field quantum Ising is replaced by a quantized local photon field. The competition among photon-hopping coupling, intrasite Rabi interaction, interaction drives system across phase transition from insulating to delocalized superradiant phase. Mean-field theory confirms existence of analytical expressions for boundary are derived. spectra excitations Bose-Einstein...
The unitary transformation method is used to study the properties of polaritonic states and Bose-Einstein condensation in Rabi lattice model, where on-site two-level systems (TLSs) are coupled with intersite hopping photons. It shown that counter-rotating coupling (CRC) between TLS photon, which breaks down conservation excitation numbers, may induce a long-range Ising-like interaction among TLSs. We have TLSs photons hybridized form states, corresponding ground state spectra calculated by...
We employ the eigen microstate approach to explore self-organized criticality (SOC) in two celebrated sandpile models, namely, BTW model and Manna model. In both phase transitions from absorbing-state critical state can be understood by emergence of dominant microstates with significantly increased weights. Spatial avalanches uniformly characterized a linear system size rescaling. The first temporal reveal scaling relations models. Furthermore, finite-size analysis microstate, we numerically...
We introduce an eigen microstate approach (EMA) in the quantum system to describe phase transition without knowing order parameter. Phases of a are determined by so-called microstates and their corresponding eigenvalues, which satisfy scaling relation critical regime. The Rabi model (QRM) is taken as example demonstrate validity EMA. Using both analytical numerical calculations, we obtain point, exponents functions superradiant QRM. It suggests that new emergency can be interpreted...
We propose the finite-size scaling of correlation function in a finite system near its critical point. At distance ${\bf r}$ with size $L$, can be written as product $|{\bf r}|^{-(d-2+η)}$ and variables r}/L$ $tL^{1/ν}$, where $t=(T-T_c)/T_c$. The directional dependence is nonnegligible only when r}|$ becomes compariable $L$. This has been confirmed by functions Ising model bond percolation two-diemnional lattices, which are calculated Monte Carlo simulation. use to determine point exponent $η$.
Full waveform inversion (FWI) of seismic traces recorded at the free surface allows reconstruction physical parameters structure on underlying medium. Our two main objectives are multiple classes one side and formulation both acoustic elastic FWI for 3D geometries. A quasi-Newtonian method with a preconditioned L-BFGS algorithm provides scaled gradients misfit function each class parameter. For onshore applications where body waves jointly inverted, P- S-wave velocities (VP VS) must be...
Abstract By training a convolutional neural network (CNN) model, we successfully recognize different phases of the El Niño-Southern oscillation. Our model achieves high recognition performance, with accuracy rates 89.4% for dataset and 86.4% validation dataset. Through statistical analysis weight parameter distribution activation output in CNN, find that most convolution kernels hidden layer neurons remain inactive, while only two play active roles. examining parameters connections between...