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Xuehua Zhao

Publications and source records attributed to Xuehua Zhao.

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Numerical analysis of an H(div)-conforming divergence-free DG method with a second-order explicit Runge-Kutta scheme for incompressible flows

Recently, H(div)-conforming DG type methods coupled with Runge-Kutta (RK) time stepping have been widely employed for simulating high Reynolds number flows, with the convective terms treated explicitly. Although the analysis techniques of RKDG methods were well developed, the extension to incompressible flows is highly nontrivial due to the exactly divergence-free constraint, where the key lies in analyzing the convective terms. We neglect viscosity effects, and conduct an error analysis for an H(div)-conforming divergence-free DG method combined with a second-order explicit RK scheme, for the incompressible Euler equations. We derive an a priori error estimate of $O(h^{k+1 / 2}+τ^2)$ under a restrictive CFL condition $τ\lesssim h^{4 / 3}$ for polynomials of degree $k \geq 1$, where $h$ and $τ$ are the mesh size and time step size, respectively, assuming that the exact solution is smooth. For the case of linear polynomials, we investigate whether existing analytical techniques can relax the restrictive CFL condition to a standard CFL condition $τ\lesssim h$. It is demonstrated that the exactly divergence-free constraint prevents the application of these techniques. We conjecture that the error estimates for linear polynomials cannot be derived under a standard CFL condition. Finally, we mention that based on our analytical framework, our analytical results will be readily extended to the Navier-Stokes equations at high mesh Reynolds number, with the viscous and convective terms treated explicitly. Numerical experiments are conducted, supporting our analytical results and the conjecture for linear polynomials.

math.NA

SSBM: A Signed Stochastic Block Model for Multiple Structure Discovery in Large-Scale Exploratory Signed Networks

Signed network structure discovery has received extensive attention and has become a research focus in the field of network science. However, most of the existing studies are focused on the networks with a single structure, e.g., community or bipartite, while ignoring multiple structures, e.g., the coexistence of community and bipartite structures. Furthermore, existing studies were faced with challenge regarding large-scale signed networks due to their high time complexity, especially when determining the number of clusters in the observed network without any prior knowledge. In view of this, we propose a mathematically principled method for signed network multiple structure discovery named the Signed Stochastic Block Model (SSBM). The SSBM can capture the multiple structures contained in signed networks, e.g., community, bipartite, and coexistence of them, by adopting a probabilistic model. Moreover, by integrating the minimum message length (MML) criterion and component-wise EM (CEM) algorithm, a scalable learning algorithm that has the ability of model selection is proposed to handle large-scale signed networks. By comparing state-of-the-art methods on synthetic and real-world signed networks, extensive experimental results demonstrate the effectiveness and efficiency of SSBM in discovering large-scale exploratory signed networks with multiple structures.

cs.SI