Searcharxiv⌕ Search

arXiv subjects

Sung Kyu Seo

Publications and source records attributed to Sung Kyu Seo.

9 recordsLinked to original sources
arXiv2026

Quantum Brownian transport in a correlated Gaussian force

We study a classical system-bath model in which a system particle is linearly coupled to a bath of harmonic oscillators. In a system subject to white and correlated Gaussian noises, we derive an expression for the joint probability density, and the mean squared values of the system-bath particles are calculated. For white noise, the mean squared values of the system-bath particles exhibit an anomalous time dependence, which is different from that of normal diffusion. In particular, for a correlated Gaussian noise, the mean squared displacement and mean squared velocity of the bath particle show superspreading growths of $t^5$ and $t^3$ in $t{\ll}τ$, respectively. When $τ=0$, the mean squared velocity of a quantum particle under random noise is proportional to $t$, while the mean squared velocity of a bath particle in the presence of correlated Gaussian noise increases in proportion to $t^2$. This anomalous transport phenomenon results from the mixed derivative structure of the master equation, which couples with the transport coordinates in the diffusion dynamics of the relative coordinates. This result shows that the removal of dissipation in the Caldeira-Leggett framework leads to fundamentally different transport mechanisms characterized by non-diffusive quantum diffusion.

cond-mat.stat-mech↗
arXiv2026

Fractional motions of an active particle on the quantum vortex

We analytically investigate the diffusive motion inferred from experimental observations of active particles driven by quantum vortices on the surface of superfluid helium. We first study the dynamical behavior of an active particle subject to a viscoelastic memory effect characterized by a power-law kernel. We then analyze the dynamics of an active particle under a uniform vortex force, thermal noise, and viscous dissipation subject to a power-law kernel. Next, by including a harmonic confining force, we obtain analytical solutions for the joint probability density in two distinct time regimes.

cond-mat.stat-mech↗
arXiv2024

Joint probability densities of an active particle coupled to two heat reservoirs

We derive a Fokker-Planck equation for joint probability density for an active particle coupled two heat reservoirs with harmonic, viscous, random forces. The approximate solution for the joint distribution density of all-to-all and three others topologies is solved, which apply an exponential correlated Gaussian force in three-time regions of correlation time. Mean squared displacement, velocity behaviors in the form of super-diffusion, while the mean squared displacement, velocity has the Gaussian form, normal diffusion. Concomitantly, the Kurtosis, correlation coefficient, and moment from moment equation are approximately and numerically calculated. In this paper, we derive an altered Fokker-Planck equation for an active particle with the harmonic, viscous, and random forces, coupled to two heat reservoirs. We attain the solution for the joint distribution density of our topology, including the center topology, the ring topology, and the chain topology, subject to an exponential correlated Gaussian force. The mean squared displacement and the mean squared velocity behavior as the super-diffusions in the short-time domain and for the characteristic time=0, while those have the Gaussian forms in the long-time domain and for the characteristic time=0. We concomitantly calculate and analyze the non-equilibrium characteristics of the kurtosis, the correlation coefficient, and the moment from the derived moment equation.

cond-mat.stat-mech↗
arXiv2024

Joint probability density with radial, tangential, and perturbative forces

We study the Fokker-Planck equation for an active particle with both the radial and tangential forces and the perturbative force. We find the solution of the joint probability density. In the limit of the long-time domain and for the characteristic time=0 domain, the mean squared radial velocity for an active particle leads to a super-diffusive distribution, while the mean squared tangential velocity with both the radial and tangential forces and the perturbative force behaviors as the Gaussian diffusion. Compared with the self-propelled particle, the mean squared tangential velocity is matched with the same value to the time ~t^2, while the mean squared radial velocity is the same as the time ~t.

cond-mat.stat-mech↗
arXiv2024

Dynamical behavior of passive particles with harmonic, viscous, and correlated Gaussian forces

In this paper, we study the Navier-Stokes equation and the Burgers equation for the dynamical motion of a passive particle with harmonic and viscous forces, subject to an exponentially correlated Gaussian force. As deriving the Fokker-Planck equation for the joint probability density of a passive particle, we find obviously the important solution of the joint probability density by using double Fourier transforms in three-time domains, and the moments from derived moment equation are numerically calculated. As a result, the dynamical motion of a passive particle with respect to the probability density having two variables of displacement and velocity in the short-time domain has a super-diffusive form, whereas the distribution in the long-time domain is obtained to be Gaussian by analyzing only from the velocity probability density.

cond-mat.stat-mech↗
arXiv2024

On the motion of passive and active particles with harmonic and viscous forces

In this paper, we solve the joint probability density for the passive and active particles with harmonic, viscous, and perturbative forces. After deriving the Fokker-Planck equation for a passive and a run-and-tumble particles, we approximately get and analyze the solution for the joint distribution density subject to an exponential correlated Gaussian force in three kinds of time limit domains. Mean squared displacement (velocity) for a particle with harmonic and viscous forces behaviors in the form of super-diffusion, consistent with a particle having viscous and perturbative forces. A passive particle with both harmonic, viscous forces and viscous, perturbative forces has the Gaussian form with mean squared velocity ~t. Particularly, In our case of a run-and-tumble particle, the mean squared displacement scales as super-diffusion, while the mean squared velocity has a normal diffusive form.In addition, the kurtosis, the correlation coefficient, and the moment from moment equation are numerically calculated.

cond-mat.stat-mech↗
arXiv2024

Joint probability density of a passive article with force and magnetic field

We firstly study the Navier-Stokes equation for the motion of a passive particle with harmonic, viscous, perturbative forces, subject to an exponentially correlated Gaussian force. Secondly, from the Fokker-Planck equation in an incompressible conducting fluid of magnetic field, we approximately obtain the solution of the joint probability density by using double Fourier transforms in three-time domains. In addition, the kurtosis, the correlation coefficient, and the moment from moment equation are numerically calculated.

cond-mat.stat-mech↗
arXiv2021

Dynamical prediction of two meteorological factors using the deep neural network and the long short term memory $(1)$

It is important to calculate and analyze temperature and humidity prediction accuracies among quantitative meteorological forecasting. This study manipulates the extant neural network methods to foster the predictive accuracy. To achieve such tasks, we analyze and explore the predictive accuracy and performance in the neural networks using two combined meteorological factors (temperature and humidity). Simulated studies are performed by applying the artificial neural network (ANN), deep neural network (DNN), extreme learning machine (ELM), long short-term memory (LSTM), and long short-term memory with peephole connections (LSTM-PC) machine learning methods, and the accurate prediction value are compared to that obtained from each other methods. Data are extracted from low frequency time-series of ten metropolitan cities of South Korea from March 2014 to February 2020 to validate our observations. To test the robustness of methods, the error of LSTM is found to outperform that of the other four methods in predictive accuracy. Particularly, as testing results, the temperature prediction of LSTM in summer in Tongyeong has a root mean squared error (RMSE) value of 0.866 lower than that of other neural network methods, while the mean absolute percentage error (MAPE) value of LSTM for humidity prediction is 5.525 in summer in Mokpo, significantly better than other metropolitan cities.

physics.ao-ph↗