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In this paper, we propose a novel mesh-free numerical method for solving the elliptic interface problems based on deep learning. We approximate the solution by the neural networks and, since the solution may change dramatically across the…

Numerical Analysis · Mathematics 2020-05-12 Cuiyu He , Xiaozhe Hu , Lin Mu

Accurate modeling of complex physical problems, such as fluid-structure interaction, requires multiphysics coupling across the interface, which often has intricate geometry and dynamic boundaries. Conventional numerical methods face…

Numerical Analysis · Mathematics 2023-08-08 Yunlong Li , Fei Wang

Non-overlapping domain decomposition methods are natural for solving interface problems arising from various disciplines, however, the numerical simulation requires technical analysis and is often available only with the use of high-quality…

Numerical Analysis · Mathematics 2023-05-18 Qi Sun , Xuejun Xu , Haotian Yi

In this paper, a meshfree method using the deep neural network (DNN) approach is developed for solving two kinds of dynamic two-phase interface problems governed by different dynamic partial differential equations on either side of the…

Numerical Analysis · Mathematics 2022-07-25 Xingwen Zhu , Xiaozhe Hu , Pengtao Sun

Scientific computing has been an indispensable tool in applied sciences and engineering, where traditional numerical methods are often employed due to their superior accuracy guarantees. However, these methods often encounter challenges…

Numerical Analysis · Mathematics 2024-07-23 Ran Bi , Jingrun Chen , Weibing Deng

This paper proposes a mesh-free computational framework and machine learning theory for solving elliptic PDEs on unknown manifolds, identified with point clouds, based on diffusion maps (DM) and deep learning. The PDE solver is formulated…

Numerical Analysis · Mathematics 2024-02-28 Senwei Liang , Shixiao W. Jiang , John Harlim , Haizhao Yang

Partial differential equations (PDEs) are often computationally challenging to solve, and in many settings many related PDEs must be be solved either at every timestep or for a variety of candidate boundary conditions, parameters, or…

Machine Learning · Computer Science 2022-11-04 Tian Qin , Alex Beatson , Deniz Oktay , Nick McGreivy , Ryan P. Adams

This work presents a generalized boundary integral method for elliptic equations on surfaces, encompassing both boundary value and interface problems. The method is kernel-free, implying that the explicit analytical expression of the kernel…

Numerical Analysis · Mathematics 2025-08-25 Pengsong Yin , Wenjun YIng , Yulin Zhang , Han Zhou

Interface problems depict many fundamental physical phenomena and widely apply in the engineering. However, it is challenging to develop efficient fully decoupled numerical methods for solving degenerate interface problems in which the…

Numerical Analysis · Mathematics 2023-06-06 Chen Fan , Zhiyue Zhang

This article presents an immersed virtual element method for solving a class of interface problems that combines the advantages of both body-fitted mesh methods and unfitted mesh methods. A background body-fitted mesh is generated…

Numerical Analysis · Mathematics 2022-09-02 Shuhao Cao , Long Chen , Ruchi Guo , Frank Lin

We show that the physics-informed neural networks (PINNs), in combination with some recently developed discontinuity capturing neural networks, can be applied to solve optimal control problems subject to partial differential equations…

Optimization and Control · Mathematics 2026-02-16 Ming-Chih Lai , Yongcun Song , Xiaoming Yuan , Hangrui Yue , Tianyou Zeng

We introduce an $r-$adaptive algorithm to solve Partial Differential Equations using a Deep Neural Network. The proposed method restricts to tensor product meshes and optimizes the boundary node locations in one dimension, from which we…

Numerical Analysis · Mathematics 2022-10-21 Ángel J. Omella , David Pardo

We present a new fixed mesh algorithm for solving a class of interface inverse problems for the typical elliptic interface problems. These interface inverse problems are formulated as shape optimization prob- lems whose objective…

Numerical Analysis · Mathematics 2018-10-18 Ruchi Guo , Tao Lin , Yanping Lin

In this paper, we develop and analyze a trilinear immersed finite element method for solving three-dimensional elliptic interface problems. The proposed method can be utilized on interface-unfitted meshes such as Cartesian grids consisting…

Numerical Analysis · Mathematics 2021-06-30 Ruchi Guo , Xu Zhang

We propose a neural network-based algorithm for solving forward and inverse problems for partial differential equations in unsupervised fashion. The solution is approximated by a deep neural network which is the minimizer of a cost…

Machine Learning · Computer Science 2019-04-12 Leah Bar , Nir Sochen

Interface problems have long been a major focus of scientific computing, leading to the development of various numerical methods. Traditional mesh-based methods often employ time-consuming body-fitted meshes with standard discretization…

Numerical Analysis · Mathematics 2023-08-09 Xurong Chi , Jingrun Chen , Zhouwang Yang

Immersed finite element (IFE) methods are a group of long-existing numerical methods for solving interface problems on unfitted meshes. A core argument of the methods is to avoid mesh regeneration procedure when solving moving interface…

Numerical Analysis · Mathematics 2020-05-01 Ruchi Guo

In this paper, a direct finite element method is proposed for solving interface problems on unfitted meshes. This new method treats the two interface conditions as an $H^{\frac12}(\Gamma)\times H^{-\frac12}(\Gamma)$ pair for the mutual…

Numerical Analysis · Mathematics 2025-08-19 Jun Hu , Limin Ma

This paper proposes a Cartesian grid-based boundary integral method for efficiently and stably solving two representative moving interface problems, the Hele-Shaw flow and the Stefan problem. Elliptic and parabolic partial differential…

Numerical Analysis · Mathematics 2026-04-22 Han Zhou , Shuwang Li , Wenjun Ying

Recently, neural networks have been extensively employed to solve partial differential equations (PDEs) in physical system modeling. While major studies focus on learning system evolution on predefined static mesh discretizations, some…

Machine Learning · Computer Science 2024-02-20 Peiyan Hu , Yue Wang , Zhi-Ming Ma
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