Panchi Li

Ansgar Jüngel, Panchi Li, Zhiwei Sun et al.

We propose a Bernoulli phase-fitted (BPF) finite difference method for the Helmholtz equation on the interval $(0, L)$ with impedance boundary conditions. The scheme is derived from a complexified Scharfetter--Gummel discretization of the one-way factorization of the Helmholtz operator. It yields both a phase-fitted interior discretization and exact discrete impedance boundary closures. For the homogeneous problem, the method is exact for plane waves, so the scheme introduces neither numerical dispersion in the interior nor artificial reflection at the boundaries. For the inhomogeneous problem, we prove well-posedness, derive wavenumber-explicit stability estimates, and establish second-order consistency and convergence valid for all $kh\notinπ\mathbb Z$, where $k$ is the wavenumber and $h$ the grid size. In particular, under the fixed-resolution condition $kh\le s_0$ for some $0<s_0<π$ together with $kL\geπ$, the constants in the error bounds remain uniform with respect to the wavenumber, yielding a pollution-free convergence theory in the principal Nyquist regime. Numerical experiments confirm the theoretical analysis and show favorable performance compared with standard and dispersion-corrected finite difference methods.

Yunjie Gong, Jingrun Chen, Rui Du et al.

The Landau-Lifshitz equation describes the dynamics of magnetization in ferromagnetic materials. Due to the essential nonlinearity and nonconvex constraint, it is typically solved numerically. In this paper, we developed a finite volume element method (FVEM) with the Gauss-Seidel projection method (GSPM) for the micromagnetics simulations. We provide the approximation error in space and depict the energy law when the FVEM is adopted. Owing to the GSPM for time-marching, the discrete system is decoupled component by component, making the computational complexity comparable to that of solving the scalar heat equation implicitly. This significantly accelerates real simulations. We present several numerical experiments to validate the theoretical analysis and the efficiency gain. Additionally, we study the blow-up solution and efficiently simulate the 2D magnetic textures using the proposed method.

Jingrun Chen, Rui Du, Panchi Li et al.

Solving partial differential equations in high dimensions by deep neural network has brought significant attentions in recent years. In many scenarios, the loss function is defined as an integral over a high-dimensional domain. Monte-Carlo method, together with the deep neural network, is used to overcome the curse of dimensionality, while classical methods fail. Often, a deep neural network outperforms classical numerical methods in terms of both accuracy and efficiency. In this paper, we propose to use quasi-Monte Carlo sampling, instead of Monte-Carlo method to approximate the loss function. To demonstrate the idea, we conduct numerical experiments in the framework of deep Ritz method proposed by Weinan E and Bing Yu. For the same accuracy requirement, it is observed that quasi-Monte Carlo sampling reduces the size of training data set by more than two orders of magnitude compared to that of MC method. Under some assumptions, we prove that quasi-Monte Carlo sampling together with the deep neural network generates a convergent series with rate proportional to the approximation accuracy of quasi-Monte Carlo method for numerical integration. Numerically the fitted convergence rate is a bit smaller, but the proposed approach always outperforms Monte Carlo method. It is worth mentioning that the convergence analysis is generic whenever a loss function is approximated by the quasi-Monte Carlo method, although observations here are based on deep Ritz method.