arXiv subjects

Feiyi Liu

Publications and source records attributed to Feiyi Liu.

12 recordsLinked to original sources

arXiv2026

Stochastic first-passage modeling of single-event burnout in SiC power MOSFETs

Single-event burnout (SEB) in silicon carbide (SiC) power MOSFETs is often characterized by deterministic threshold quantities. Near the boundary between recovery and runaway, stochastic variability can make this threshold description probabilistic rather than sharp. This work introduces a first-passage perspective for stochastic threshold broadening in burnout. The process is described by a reduced electrothermal feedback-relaxation model with an absorbing boundary. The model combines carrier multiplication, avalanche feedback, localized heating, carrier loss, and thermal relaxation. Stochastic carrier and thermal terms represent unresolved event-level variability. The main finding is that finite fluctuations broaden the deterministic burnout threshold into a probabilistic transition band. Noise-induced subthreshold runaway also emerges, where nominally recoverable conditions can still fail through rare stochastic excursions. First-passage-time distributions resolve the time scale of burnout and survival probabilities further distinguish rapid feedback-dominated runaway from delayed stochastic failure. A feedback-relaxation phase diagram organizes recoverable, probabilistic, and rapidly unstable regimes. This framework provides a statistical-physics interpretation of threshold dispersion in single-event burnout of SiC power MOSFETs by linking coarse-grained electrothermal dynamics to probabilistic and time-resolved failure observables.

cond-mat.stat-mech↗

arXiv2026

Correlated decoherence in a common environment activated by relative motion

We study two spatially separated boundary subsystems coupled to a common structured environment under relative motion within a Gaussian open-system framework. By integrating out the environment, we obtain an influence functional governed by a dressed environmental correlator evaluated at the boundary positions. Its retarded and Hadamard components govern coherent mediation and correlated fluctuations, respectively. Relative motion activates the leading resonant contribution to correlated decoherence through Doppler-shifted spectral overlap of the boundary excitations. For identical boundary dispersions, this contribution has an ideal narrow-linewidth onset at $v>2u_\phi$. Below this value, no resonant momentum shell exists, whereas above it a finite shell opens and produces an enhanced correlated-decoherence contribution. Finite spectral linewidth generates subthreshold tails and rounds the ideal onset into a crossover centered on the same kinematic condition. The leading resonant contributions to motion-induced excitation production and correlated decoherence probe the same on-shell environmental structure. The proposed readout of this two-subsystem reduced-dynamics signature uses either differential-coherence measurements or a two-node noise cross-spectrum.

quant-ph↗

arXiv2026

Non-Markovian Entropy Dynamics in Living Systems from the Keldysh Formalism

Living systems are open nonequilibrium systems that continuously exchange energy, matter, and information with their environments, leading to stochastic dynamics with memory and active fluctuations. In this study, we develop a non-Markovian theoretical framework for the entropy dynamics of living systems based on the Keldysh functional formalism and stochastic thermodynamics. The approach naturally incorporates colored environmental noise, memory-dependent dissipation, and many-body interactions, yielding generalized Langevin dynamics and non-Markovian master equations. Within this framework we derive an exact frequency-domain expression for the entropy production rate and show that violations of the fluctuation-dissipation relation provide a direct thermodynamic signature of active biological fluctuations. We further demonstrate that environmental memory enhances low-frequency fluctuations and entropy production, leading to critical slowing down near dynamical instability. These results provide a microscopic physical foundation for the entropy "bathtub" picture of living systems and connect entropy evolution with development, aging, and death in nonequilibrium dynamics.

cond-mat.stat-mech↗

arXiv2024

Motion-driven quantum dissipation in an open electronic system with nonlocal interaction

In this paper, we study excitations and dissipation in two infinite parallel metallic plates undergoing relative motion. The degrees of freedom of the electrons in both plates are modeled using the 1+2 dimensional Dirac field, and a nonlocal potential is selected to describe the interaction between the two plates. The internal relative motion is introduced via a Galilean boost, with one plate assumed to slide relative to the other. We then calculate the effective action of the system and derive the vacuum occupation number in momentum space using a perturbative method. Numerical plots reveal that the vacuum occupation number, as a function of momentum, is isotropic for a motion speed $v = 0$ and anisotropic for nonzero $v$. The relative motion induces energy transfer between the plates, leading to on-shell excitations in a manner analogous to the dissipative process of the Schwinger effect. Consequently, we study the motion-induced dissipation effects and the dissipative forces through the quantum action. Numerical results demonstrate that both the imaginary part of the quantum action due to the motion boost and the dissipative force exhibit a threshold as functions of $v$, and both are positively correlated with $v$.

quant-ph↗

arXiv2024

The magnon mediated plasmon friction: a functional integral approach

In this paper, we discuss quantum friction in a system formed by two metallic surfaces separated by a ferromagnetic intermedium of a certain thickness. The internal degrees of freedom in the two metallic surfaces are assumed to be plasmons, while the excitations in the intermediate material are magnons, modeling plasmons coupled to magnons. During relative sliding, one surface moves uniformly parallel to the other, causing friction in the system. By calculating the effective action of the magnons, we can determine the particle production probability, which shows a positive correlation between the probability and the sliding speed. Finally, we derive the frictional force of the system, with both theoretical and numerical results indicating that the friction, like the particle production probability, also has a positive correlation with the speed.

cond-mat.stat-mech↗

arXiv2024

Learning phase transitions by siamese neural network

The wide application of machine learning (ML) techniques in statistics physics has presented new avenues for research in this field. In this paper, we introduce a semi-supervised learning method based on Siamese Neural Networks (SNN), trying to explore the potential of neural network (NN) in the study of critical behaviors beyond the approaches of supervised and unsupervised learning. By focusing on the (1+1) dimensional bond directed percolation (DP) model of nonequilibrium phase transition and the 2 dimensional Ising model of equilibrium phase transition, we use the SNN to predict the critical values and critical exponents of the systems. Different from traditional ML methods, the input of SNN is a set of configuration data pairs and the output prediction is similarity, which prompts to find an anchor point of data for pair comparison during the test. In our study, during test we set different bond probability $p$ or temperature $T$ as anchors, and discuss the impact of the configurations at this anchors on predictions. In addition, we use an iterative method to find the optimal training interval to make the algorithm more efficient, and the prediction results are comparable to other ML methods.

physics.comp-ph↗

arXiv2024

Machine learning of pair-contact process with diffusion

The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition. Unlike the PCP, whose nature of phase transition is clearly classified into the directed percolation (DP) universality class, the model of PCPD has been controversially discussed since its infancy. To our best knowledge, there is so far no consensus on whether the phase transition of the PCPD falls into the unknown university classes or else conveys a new kind of non-equilibrium phase transition. In this paper, both unsupervised and supervised learning are employed to study the PCPD with scrutiny. Firstly, two unsupervised learning methods, principal component analysis (PCA) and autoencoder, are taken. Our results show that both methods can cluster the original configurations of the model and provide reasonable estimates of thresholds. Therefore, no matter whether the non-equilibrium lattice model is a random process of unitary (for instance the DP) or binary (for instance the PCP), or whether it contains the diffusion motion of particles, unsupervised leaning can capture the essential, hidden information. Beyond that, supervised learning is also applied to learning the PCPD at different diffusion rates. We proposed a more accurate numerical method to determine the spatial correlation exponent $ν_{\perp}$, which, to a large degree, avoids the uncertainty of data collapses through naked eyes. Our extensive calculations reveal that $ν_{\perp}$ of PCPD depends continuously on the diffusion rate $D$, which supports the viewpoint that the PCPD may lead to a new type of absorbing phase transition.

cond-mat.stat-mech↗

arXiv2024

Applications of Domain Adversarial Neural Network in phase transition of 3D Potts model

Machine learning techniques exhibit significant performance in discriminating different phases of matter and provide a new avenue for studying phase transitions. We investigate the phase transitions of three dimensional $q$-state Potts model on cubic lattice by using a transfer learning approach, Domain Adversarial Neural Network (DANN). With the unique neural network architecture, it could evaluate the high-temperature (disordered) and low-temperature (ordered) phases, and identify the first and second order phase transitions. Meanwhile, by training the DANN with a few labeled configurations, the critical points for $q=2,3,4$ and $5$ can be predicted with high accuracy, which are consistent with those of the Monte Carlo simulations. These findings would promote us to learn and explore the properties of phase transitions in high-dimensional systems.

physics.comp-ph↗

arXiv2024

Study of phase transition of Potts model with Domain Adversarial Neural Network

A transfer learning method, Domain Adversarial Neural Network (DANN), is introduced to study the phase transition of two-dimensional q-state Potts model. With the DANN, we only need to choose a few labeled configurations automatically as input data, then the critical points can be obtained after training the algorithm. By an additional iterative process, the critical points can be captured to comparable accuracy to Monte Carlo simulations as we demonstrate it for q = 3, 4, 5, 7 and 10. The type of phase transition (first or second-order) is also determined at the same time. Meanwhile, for the second-order phase transition at q=3, we can calculate the critical exponent $ν$ by data collapse. Furthermore, compared to the traditional supervised learning, we found the DANN to be more accurate with lower cost.

cond-mat.stat-mech↗

arXiv2024

Supervised and unsupervised learning of (1+1)-dimensional even-offspring branching annihilating random walks

Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically. The combination of ML and most renowned nonequilibrium model, directed percolation (DP), led to some significant findings. In this study, ML is applied to (1+1)-d, even offspring branching annihilating random walks (BAW), whose universality class is not DP-like. The supervised learning of (1+1)-d BAW via convolutional neural networks (CNN) results in a more accurate prediction of the critical point than the Monte Carlo (MC) simulation for the same system sizes. The dynamic exponent \;$z$\; and spatial correlation length correlation exponent \;$ν_{\perp}$\ were also measured and found to be consistent with their respective theoretical values. Furthermore, the unsupervised learning of (1+1)-d BAW via an autoencoder (AE) gives rise to a transition point, which is the same as the critical point. The latent layer of AE, through a single neuron, can be regarded as the order parameter of the system being properly re-scaled. Therefore, we believe that ML has exciting application prospects in reaction-diffusion systems such as BAW and DP.

nlin.CG↗

arXiv2023

Jet tagging algorithm of graph network with HaarPooling message passing

Recently methods of graph neural networks (GNNs) have been applied to solving the problems in high energy physics (HEP) and have shown its great potential for quark-gluon tagging with graph representation of jet events. In this paper, we introduce an approach of GNNs combined with a HaarPooling operation to analyze the events, called HaarPooling Message Passing neural network (HMPNet). In HMPNet, HaarPooling not only extracts the features of graph, but embeds additional information obtained by clustering of k-means of different particle features. We construct Haarpooling from five different features: absolute energy $\log E$, transverse momentum $\log p_T$, relative coordinates $(Δη,Δϕ)$, the mixed ones $(\log E, \log p_T)$ and $(\log E, \log p_T, Δη,Δϕ)$. The results show that an appropriate selection of information for HaarPooling enhances the accuracy of quark-gluon tagging, as adding extra information of $\log P_T$ to the HMPNet outperforms all the others, whereas adding relative coordinates information $(Δη,Δϕ)$ is not very effective. This implies that by adding effective particle features from HaarPooling can achieve much better results than solely pure message passing neutral network (MPNN) can do, which demonstrates significant improvement of feature extraction via the pooling process. Finally we compare the HMPNet study, ordering by $p_T$, with other studies and prove that the HMPNet is also a good choice of GNN algorithms for jet tagging.

hep-ex↗

arXiv2022

Transfer learning of phase transitions in percolation and directed percolation

The latest advances of statistical physics have shown remarkable performance of machine learning in identifying phase transitions. In this paper, we apply domain adversarial neural network (DANN) based on transfer learning to studying non-equilibrium and equilibrium phase transition models, which are percolation model and directed percolation (DP) model, respectively. With the DANN, only a small fraction of input configurations (2d images) needs to be labeled, which is automatically chosen, in order to capture the critical point. To learn the DP model, the method is refined by an iterative procedure in determining the critical point, which is a prerequisite for the data collapse in calculating the critical exponent $ν_{\perp}$. We then apply the DANN to a two-dimensional site percolation with configurations filtered to include only the largest cluster which may contain the information related to the order parameter. The DANN learning of both models yields reliable results which are comparable to the ones from Monte Carlo simulations. Our study also shows that the DANN can achieve quite high accuracy at much lower cost, compared to the supervised learning.

cond-mat.stat-mech↗