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Data assimilation (DA) methods use priors arising from differential equations to robustly interpolate and extrapolate data. Popular techniques such as ensemble methods that handle high-dimensional, nonlinear PDE priors focus mostly on state…

Machine Learning · Statistics 2024-06-05 Rafael Anderka , Marc Peter Deisenroth , So Takao

In the framework of ODEs, we uncover a new link between the continuous Galerkin method (see Math. Comp. (1972), 26 (118 and 120), 415-426 and 881-891) and the discontinuous Galerkin method (see Mathematical Aspects of Finite elements in…

Numerical Analysis · Mathematics 2025-09-29 Bernardo Cockburn

Standard discontinuous Galerkin methods, based on piecewise polynomials of degree $ \qq=0,1$, are considered for temporal semi-discretization for second order hyperbolic equations. The main goal of this paper is to present a simple and…

Numerical Analysis · Mathematics 2022-10-19 Neda Rezaei , Fardin Saedpanah

Data assimilation combines prior (or background) information with observations to estimate the initial state of a dynamical system over a given time-window. A common application is in numerical weather prediction where a previous forecast…

Optimization and Control · Mathematics 2021-07-27 Coralia Cartis , Maha H. Kaouri , Amos S. Lawless , Nancy K. Nichols

A convergence result for a discontinuous Galerkin multiscale method for a second order elliptic problem is presented. We consider a heterogeneous and highly varying diffusion coefficient in $L^\infty(\Omega,\mathbb{R}^{d\times d}_{sym})$…

Numerical Analysis · Mathematics 2012-11-26 Daniel Elfverson , Emmanuil H. Georgoulis , Axel Målqvist , Daniel Peterseim

Concurrent observation technologies have made high-precision real-time data available in large quantities. Data assimilation (DA) is concerned with how to combine this data with physical models to produce accurate predictions. For…

Numerical Analysis · Mathematics 2020-08-26 Jana de Wiljes , Xin T. Tong

Recent work has focused on data-driven learning of the evolution of unknown systems via deep neural networks (DNNs), with the goal of conducting long time prediction of the evolution of the unknown system. Training a DNN with low…

Machine Learning · Computer Science 2022-12-28 Victor Churchill , Steve Manns , Zhen Chen , Dongbin Xiu

This paper presents a fully discrete numerical scheme for one-dimensional nonlocal wave equations and provides a rigorous theoretical analysis. To facilitate the spatial discretization, we introduce an auxiliary variable analogous to the…

Numerical Analysis · Mathematics 2025-07-15 Qiang Du , Kui Ren , Lu Zhang , Yin Zhou

In this paper, we consider a class of discontinuous Galerkin (DG) methods for one-dimensional nonlocal diffusion (ND) problems. The nonlocal models, which are integral equations, are widely used in describing many physical phenomena with…

Numerical Analysis · Mathematics 2024-08-15 Qiang Du , Lili Ju , Jianfang Lu , Xiaochuan Tian

This paper is concerned with developing accurate and efficient numerical methods for fully nonlinear second order elliptic and parabolic partial differential equations (PDEs) in multiple spatial dimensions. It presents a general framework…

Numerical Analysis · Mathematics 2018-01-19 Xiaobing Feng , Thomas Lewis

The discontinuous Galerkin (DG) method is an established method for computing approximate solutions of partial differential equations in many applications. Unlike continuous finite elements, in DG methods, numerical fluxes are used to…

Numerical Analysis · Mathematics 2019-12-02 Kenneth Duru , Leonhard Rannabauer , Alice-Agnes Gabriel , Heiner Igel

We present the recent development of hybridizable and embedded discontinuous Galerkin (DG) methods for wave propagation problems in fluids, solids, and electromagnetism. In each of these areas, we describe the methods, discuss their main…

Numerical Analysis · Mathematics 2018-07-03 Pablo Fernandez , Alexandra Christophe , Sebastien Terrana , Ngoc-Cuong Nguyen , Jaime Peraire

The scalability of time-dependent partial differential equation (PDE) solvers based on the discontinuous Galerkin (DG) method is increasingly limited by data communication and synchronization requirements across processing elements (PEs) at…

Computational Physics · Physics 2026-03-31 Shubham Kumar Goswami , Dapse Vidyesh , Konduri Aditya

A new finite element method with discontinuous approximation is introduced for solving second order elliptic problem. Since this method combines the features of both conforming finite element method and discontinuous Galerkin (DG) method,…

Numerical Analysis · Mathematics 2019-04-09 Xiu Ye , Shangyou Zhang

The discontinuous Galerkin (DG) time-stepping method applied to abstract evolution equation of parabolic type is studied using a variational approach. We establish the inf-sup condition or Babu\v{s}ka--Brezzi condition for the DG bilinear…

Numerical Analysis · Mathematics 2020-01-24 Norikazu Saito

Ultra-rapid data assimilation (URDA) is a method that rapidly updates preemptive forecasts derived from observations without integrating a dynamical model each time additional observations become available. Due to its computational…

Geophysics · Physics 2026-05-19 Fumitoshi Kawasaki , Atsushi Okazaki , Kenta Kurosawa , Shunji Kotsuki

Gravitational wave emission from extreme mass ratio binaries (EMRBs) should be detectable by the joint NASA-ESA LISA project, spurring interest in analytical and numerical methods for investigating EMRBs. We describe a discontinuous…

General Relativity and Quantum Cosmology · Physics 2009-11-19 Scott E. Field , Jan S. Hesthaven , Stephen R. Lau

Entropy stable discontinuous Galerkin (DG) methods improve the robustness of high order DG simulations of nonlinear conservation laws. These methods yield a semi-discrete entropy inequality, and rely on an algebraic flux differencing…

Numerical Analysis · Mathematics 2025-07-03 Jesse Chan

We present the mathematical framework of a Domain Decomposition (DD) aproach based on Parallel-in-Time methods (PinT-based approach) for solving the 4D-Var Data Assimilation (DA) model. The main outcome of the proposed DD PinT-based…

Numerical Analysis · Mathematics 2018-07-20 Luisa D'Amore , Rosalba Cacciapuoti

This paper is concerned with developing accurate and efficient numerical methods for one-dimensional fully nonlinear second order elliptic and parabolic partial differential equations (PDEs). In the paper we present a general framework for…

Numerical Analysis · Mathematics 2012-12-04 Xiaobing Feng , Thomas Lewis