arXiv subjects

Yihua Li

Publications and source records attributed to Yihua Li.

6 recordsLinked to original sources

arXiv2025

Data-constrained magnetohydrodynamic simulation of global solar corona including solar wind effects within 2.5 $R_\odot$

Total solar eclipses (TSEs) provide a unique opportunity to observe the large-scale solar corona. The solar wind plays an important role in forming the large-scale coronal structure and magnetohydrodynamic (MHD) simulations are used to reproduce it for further studying coronal mass ejections (CMEs). We conduct a data-constrained MHD simulation of the global solar corona including solar wind effects of the 2024 April 8 TSE with observed magnetograms using the Message Passing Interface Adaptive Mesh Refinement Versatile Advection Code (MPI-AMRVAC) within 2.5 $R_\odot$. This TSE happened within the solar maximum, hence the global corona was highly structured. Our MHD simulation includes the energy equation with a reduced polytropic index $γ=1.05$. We compare the global magnetic field for multiple magnetograms and use synchronic frames from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager to initialize the magnetic field configuration from a magneto-frictionally equilibrium solution, called the Outflow field. We detail the initial and boundary conditions employed to time-advance the full set of ideal MHD equations such that the global corona is relaxed to a steady state. The magnetic field, the velocity field, and distributions of the density and thermal pressure are successfully reproduced. We demonstrate direct comparisons with TSE images in white-light and Fe XIV emission augmented with quasi-separatrix layers, the integrated current density, and the synthetic white-light radiation, and find a good agreement between simulations and observations. This provides a fundamental background for future simulations to study the triggering and acceleration mechanisms of CMEs under solar wind effects.

astro-ph.SR↗

arXiv2025

A purely Quantum Generative Modeling through Unitary Scrambling and Collapse

Quantum computing offers fundamentally more expressive mechanisms for generative modeling, yet current approaches remain constrained by classical neural components that bottleneck quantum capability and hardware efficiency. We propose the Quantum Scrambling and Collapse Generative Model (QGen), a purely quantum paradigm that eliminates classical dependencies. QGen implements two coherent processes: scrambling, which interleaves Gaussian diffusion channels with unitary delocalization to disperse information globally while avoiding collapse into uninformative states; and collapse, where parameterized quantum circuits refocus scrambled distributions into structured outputs, achieving distributional reconstruction under coherent evolution. To enable scalability, we introduce a measurement-based training principle that decomposes learning into tractable subproblems, mitigating barren plateaus. Empirically, QGen outperforms classical and hybrid baselines under matched parameter budget, while maintaining robustness under finite-shot sampling, demonstrating strong feasibility for near-term hardware.

quant-ph↗

arXiv2025

Data-constrained Magnetohydrodynamic Simulation of a Filament Eruption in a Decaying Active Region 13079 on a Global Scale

Filaments are special plasma phenomena embedded in the solar atmosphere, characterized by unique thermodynamic properties and magnetic structures. Magnetohydrodynamic (MHD) simulations are useful to investigate the eruption mechanisms of filaments. We conduct a data-constrained zero-$β$ MHD simulation in spherical coordinates to investigate a C3.5 class flare triggered by an eruptive filament on 2022 August 15 in a decaying weak active region 13079. We reconstruct the three-dimensional coronal magnetic field using vector magnetograms and synoptic maps from the Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI). We transform vector magnetic field into Stonyhurst heliographic spherical coordinates combined with a synoptic map and constructed a potential field source surface (PFSS) model with a magnetic flux rope (MFR) embedded using the Regularized Biot--Savart Laws (RBSL). Subsequently, we conduct a spherical zero-$β$ MHD simulation using the Message Passing Interface Adaptive Mesh Refinement Versatile Advection Code (MPI-AMRVAC) and replicated the entire dynamic process of the filament eruption consistent with observations. With the calculation of time-distance profile, Qusai-Separatrix Layers (QSL), and synthetic radiation from simulated current density, we find a good agreement between our simulation and observations in terms of dynamics and magnetic topology. Technically, we provide a useful method of advanced data-constrained simulation of weak active regions in spherical coordinates. Scientifically, the model allows us to quantitatively describe and diagnose the entire process of filament eruption.

astro-ph.SR↗

arXiv2024

Low-Latency Layer-Aware Proactive and Passive Container Migration in Meta Computing

Meta computing is a new computing paradigm that aims to efficiently utilize all network computing resources to provide fault-tolerant, personalized services with strong security and privacy guarantees. It also seeks to virtualize the Internet as many meta computers. In meta computing, tasks can be assigned to containers at edge nodes for processing, based on container images with multiple layers. The dynamic and resource-constrained nature of meta computing environments requires an optimal container migration strategy for mobile users to minimize latency. However, the problem of container migration in meta computing has not been thoroughly explored. To address this gap, we present low-latency, layer-aware container migration strategies that consider both proactive and passive migration. Specifically: 1) We formulate the container migration problem in meta computing, taking into account layer dependencies to reduce migration costs and overall task duration by considering four delays. 2) We introduce a reinforcement learning algorithm based on policy gradients to minimize total latency by identifying layer dependencies for action selection, making decisions for both proactive and passive migration. Expert demonstrations are introduced to enhance exploitation. 3) Experiments using real data trajectories show that the algorithm outperforms baseline algorithms, achieving lower total latency.

cs.DC↗

arXiv2022

Surface engineering for ultrathin metal anodes enabling high-performance Zn-ion batteries

Zn metal battery has been considered a promising alternative energy storage technology in renewable energy storage and grid storage. It is well-known that the surface orientation of a Zn metal anode is vital to the reversibility of a Zn metal battery. Herein, the (101)-oriented thin Zn metal anode (down to 2 μm) is electrodeposited on a Cu surface by adding dimethyl sulfoxide (DMSO) electrolyte additive in ZnSO4 aqueous solution. Scanning electron microscope (SEM) observation indicates the formation of flat terrace-like compact (101)-oriented surfaces. Insitu optical observation confirms that the (101)-oriented surfaces can be reversibly plated and stripped. DFT calculations reveal two mechanisms for the nucleation and growth of the Zn-(101) surface: (1) formation of Zn(101)//Cu(001) could lower the interface energy as compared to Zn(002)//Cu(001); (2) large reconstruction of the Zn (101) surface with DMSO and H2O absorption. Raman, XPS, and ToF-SIMS characterizations indicate that adding DMSO in ZnCl2 could facilitate the formation of ZnO-based SEI on Zn metal surface, while OH- and S-based SEI can be obtained with DMSO in ZnSO4. The electrochemical testings are performed, which demonstrates a higher cyclability for the (101)-oriented Zn in the half cell as well as a lower charge transfer barrier with respect to the (002)-dominated surface of the same electrode thickness. Zn||V2O5 full cells are further assembled, showing better capacity retention for the (101)-Zn as compared to the (002)-Zn with the same thickness (5 μm, 3 μm, and 2 μm). We hope this study to spur further interest in the control of Zn metal surface crystallographic orientation towards ultrathin Zn metal anodes.

cond-mat.mtrl-sci↗

arXiv2019

Nearest Neighbors for Matrix Estimation Interpreted as Blind Regression for Latent Variable Model

We consider the setup of nonparametric {\em blind regression} for estimating the entries of a large $m \times n$ matrix, when provided with a small, random fraction of noisy measurements. We assume that all rows $u \in [m]$ and columns $i \in [n]$ of the matrix are associated to latent features $x_{\text{row}}(u)$ and $x_{\text{col}}(i)$ respectively, and the $(u,i)$-th entry of the matrix, $A(u, i)$ is equal to $f(x_{\text{row}}(u), x_{\text{col}}(i))$ for a latent function $f$. Given noisy observations of a small, random subset of the matrix entries, our goal is to estimate the unobserved entries of the matrix as well as to "de-noise" the observed entries. As the main result of this work, we introduce a nearest-neighbor-based estimation algorithm, and establish its consistency when the underlying latent function $f$ is Lipschitz, the underlying latent space is a bounded diameter Polish space, and the random fraction of observed entries in the matrix is at least $\max \left( m^{-1 + δ}, n^{-1/2 + δ} \right)$, for any $δ> 0$. As an important byproduct, our analysis sheds light into the performance of the classical collaborative filtering algorithm for matrix completion, which has been widely utilized in practice. Experiments with the MovieLens and Netflix datasets suggest that our algorithm provides a principled improvement over basic collaborative filtering and is competitive with matrix factorization methods. Our algorithm has a natural extension to the setting of tensor completion via flattening the tensor to matrix. When applied to the setting of image in-painting, which is a $3$-order tensor, we find that our approach is competitive with respect to state-of-art tensor completion algorithms across benchmark images.

math.ST↗