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Weiwen Wang

Publications and source records attributed to Weiwen Wang.

3 recordsLinked to original sources

Decoupled energy-stable Runge-Kutta schemes of arbitrary order for the anisotropic phase-field dendritic crystal growth model

In this paper, we construct an arbitrary-order scheme for the anisotropic phase-field dendritic crystal growth model by introducing a time dependent auxiliary variable. By employing an algebraically stable Runge-Kutta method, the proposed scheme satisfies an unconditional discrete energy dissipation law. To reduce the computational cost, a matrix diagonalization technique is applied to the coupled elliptic system at each time step. This transforms the original system into independent elliptic equations with constant coefficients, which can be solved separately or in parallel. After the decoupling, the auxiliary variable is obtained from a uniquely solvable $q\times q$ algebraic system. For a fixed Fourier-Galerkin space, we further prove $q$th-order convergence in time for the scheme based on a $q$-stage Runge-Kutta method. Numerical experiments in two and three dimensions confirm the theoretical convergence rates and the discrete energy dissipation, and demonstrate the computational efficiency of the decoupled schemes. The effects of anisotropy, latent heat, orientation angle, and initial nuclei on the dendritic morphology are also investigated numerically.

math.NA↗

An efficient fully explicit scheme for stochastic Navier-Stokes equations driven by multiplicative noise

This work proposes an efficient, linear, and fully decoupled pressure-correction scheme for the 2D stochastic Navier-Stokes equations with multiplicative noise and Dirichlet boundary condition. Leveraging the auxiliary variable approach, the scheme is fully explicit yet unconditionally stable. At each time step, it only requires solving Poisson-type equations with constant coefficients. To the best of our knowledge, this is the first application of the auxiliary variable method to stochastic Navier-Stokes equations. We provide a detailed strong convergence analysis for the linearized equation under standard assumptions.

math.NA↗

Invariant Risk Minimization Is A Total Variation Model

Invariant risk minimization (IRM) is an arising approach to generalize invariant features to different environments in machine learning. While most related works focus on new IRM settings or new application scenarios, the mathematical essence of IRM remains to be properly explained. We verify that IRM is essentially a total variation based on $L^2$ norm (TV-$\ell_2$) of the learning risk with respect to the classifier variable. Moreover, we propose a novel IRM framework based on the TV-$\ell_1$ model. It not only expands the classes of functions that can be used as the learning risk and the feature extractor, but also has robust performance in denoising and invariant feature preservation based on the coarea formula. We also illustrate some requirements for IRM-TV-$\ell_1$ to achieve out-of-distribution generalization. Experimental results show that the proposed framework achieves competitive performance in several benchmark machine learning scenarios.

cs.LG↗