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We introduce a space-time Trefftz discontinuous Galerkin method for the first-order transient acoustic wave equations in arbitrary space dimensions, extending the one dimensional scheme of Kretzschmar et al. (2016, IMA J. Numer. Anal., 36,…

Numerical Analysis · Mathematics 2022-08-29 Andrea Moiola , Ilaria Perugia

We propose a new geometrically unfitted finite element method based on discontinuous Trefftz ansatz spaces. Trefftz methods allow for a reduction in the number of degrees of freedom in discontinuous Galerkin methods, thereby, the costs for…

Numerical Analysis · Mathematics 2023-10-12 Fabian Heimann , Christoph Lehrenfeld , Paul Stocker , Henry von Wahl

We present new convergence analyses for parallel subspace correction methods for unconstrained semicoercive and nearly semicoercive convex optimization problems, generalizing the theory of singular and nearly singular linear problems to a…

Numerical Analysis · Mathematics 2026-04-03 Young-Ju Lee , Jongho Park

A space-time Trefftz discontinuous Galerkin method for the Schr\"odinger equation with piecewise-constant potential is proposed and analyzed. Following the spirit of Trefftz methods, trial and test spaces are spanned by non-polynomial…

Numerical Analysis · Mathematics 2023-10-03 Sergio Gómez , Andrea Moiola

Computing many eigenpairs of the Schr{\"o}dinger operator presents a computational bottleneck in large-scale quantum simulations due to the global communication overhead of explicit orthogonalization. To address this issue, we propose a…

Numerical Analysis · Mathematics 2026-05-26 Shengyue Wang , Aihui Zhou

We study an embedded Trefftz discontinuous Galerkin method for the Helmholtz equation. The method starts from a polynomial DG space and enforces the Trefftz property through local constraints, avoiding an explicit construction of Trefftz…

Numerical Analysis · Mathematics 2026-03-16 Paul Stocker , Igor Voulis

This work considers stochastic Galerkin approximations of linear elliptic partial differential equations (PDEs) with stochastic forcing terms and stochastic diffusion coefficients, that cannot be bounded uniformly away from zero and…

Numerical Analysis · Mathematics 2026-01-12 Fabio Musco , Andrea Barth

We present a space-time ultra-weak discontinuous Galerkin discretization of the linear Schr\"odinger equation with variable potential. The proposed method is well-posed and quasi-optimal in mesh-dependent norms for very general discrete…

Numerical Analysis · Mathematics 2024-03-05 Sergio Gómez , Andrea Moiola

In this article we develop convergence theory for a general class of adaptive approximation algorithms for abstract nonlinear operator equations on Banach spaces, and use the theory to obtain convergence results for practical adaptive…

Numerical Analysis · Mathematics 2010-01-12 Michael Holst , Gantumur Tsogtgerel , Yunrong Zhu

Summary This work presents variational concepts associated with reduced Trefftz type approaches and discusses the interrelationship between various concepts of the displacement, hybrid and Trefftz methods. The basic concept of the…

Computational Engineering, Finance, and Science · Computer Science 2017-06-27 Sulaiman Abo Diab

We study the approximation properties of complex-valued polynomial Trefftz spaces for the $(d+1)$-dimensional linear time-dependent Schr\"odinger equation. More precisely, we prove that for the space-time Trefftz discontinuous Galerkin…

Numerical Analysis · Mathematics 2023-10-03 Sergio Gómez , Andrea Moiola , Ilaria Perugia , Paul Stocker

In this work, we study a global quadrature scheme for analytic functions on compact intervals based on function values on quasi-uniform grids of quadrature nodes. In practice it is not always possible to sample functions at optimal nodes…

Numerical Analysis · Mathematics 2022-11-28 Giacomo Cappellazzo , Wolfgang Erb , Francesco Marchetti , Davide Poggiali

We propose an abstract discontinuous Galerkin neural network (DGNN) framework for analyzing the convergence of least-squares methods based on the residual minimization when feasible solutions are neural networks. Within this framework, we…

Numerical Analysis · Mathematics 2025-11-11 Long Yuan , Hongxing Rui

A local weighted discontinuous Galerkin gradient discretization method for solving elliptic equations is introduced. The local scheme is based on a coarse grid and successively improves the solution solving a sequence of local elliptic…

Numerical Analysis · Mathematics 2018-07-30 Assyr Abdulle , Giacomo Rosilho de Souza

We apply polynomial approximation methods -- known in the numerical PDEs context as spectral methods -- to approximate the vector-valued function that satisfies a linear system of equations where the matrix and the right hand side depend on…

Numerical Analysis · Mathematics 2013-05-21 Paul G. Constantine , David F. Gleich , Gianluca Iaccarino

We propose a nonlocal operator method for solving partial differential equations (PDEs). The nonlocal operator is derived from the Taylor series expansion of the unknown field, and can be regarded as the integral form "equivalent" to the…

Computational Physics · Physics 2019-02-04 Huilong Ren , Xiaoying Zhuang , Timon Rabczuk

While deep learning has achieved remarkable success in solving partial differential equations (PDEs), it still faces significant challenges, particularly when the PDE solutions have low regularity or singularities. To address these issues,…

Numerical Analysis · Mathematics 2025-06-19 Zhihang Xu , Min Wang , Zhu Wang

We propose a Trefftz discontinuous Galerkin (TDG) method for the approximation of plane wave scattering by periodic diffraction gratings, modelled by the two-dimensional Helmholtz equation. The periodic obstacle may include penetrable and…

Numerical Analysis · Mathematics 2026-02-06 Armando Maria Monforte , Andrea Moiola

In this contribution, kernel approximations are applied as ansatz functions within the Deep Ritz method. This allows to approximate weak solutions of elliptic partial differential equations with weak enforcement of boundary conditions using…

Numerical Analysis · Mathematics 2024-10-07 Hendrik Kleikamp , Tizian Wenzel

A kernel-based approach for the learning of the solution operator of general nonhomogeneous partial differential equations (PDEs) is proposed. The method incorporates physical priors, typically encoded through the PDE operator, into a…

Numerical Analysis · Mathematics 2026-05-12 Jianyu Hu , Juan-Pablo Ortega