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POD-DL-ROMs have been recently proposed as an extremely versatile strategy to build accurate and reliable reduced order models (ROMs) for nonlinear parametrized partial differential equations, combining (i) a preliminary dimensionality…

Numerical Analysis · Mathematics 2023-05-09 Simone Brivio , Stefania Fresca , Nicola Rares Franco , Andrea Manzoni

We develop new dynamically orthogonal tensor methods to approximate multivariate functions and the solution of high-dimensional time-dependent nonlinear partial differential equations (PDEs). The key idea relies on a hierarchical…

Numerical Analysis · Mathematics 2020-01-29 Alec Dektor , Daniele Venturi

Reduced-order models have long been used to understand the behavior of nonlinear partial differential equations (PDEs). Naturally, reduced-order modeling techniques come at the price of computational accuracy for a decrease in computation…

Numerical Analysis · Mathematics 2023-07-26 Jovan Žigić

We study the quantitative homogenization of linear second order elliptic equations in non-divergence form with highly oscillating periodic diffusion coefficients and with large drifts, in the so-called ``centered'' setting where…

Analysis of PDEs · Mathematics 2023-07-10 Wenjia Jing , Yiping Zhang

In this paper, we propose a model reduction method for solving multiscale elliptic PDEs with random coefficients in the multiquery setting using an optimization approach. The optimization approach enables us to construct a set of localized…

Numerical Analysis · Mathematics 2018-07-09 Thomas Y. Hou , Dingjiong Ma , Zhiwen Zhang

This paper describes a massively parallel algebraic multigrid method based on non-smoothed aggregation. It is especially suited for solving heterogeneous elliptic problems as it uses a greedy heuristic algorithm for the aggregation that…

Numerical Analysis · Mathematics 2013-10-01 Markus Blatt , Olaf Ippisch , Peter Bastian

We extend the piecewise orthogonal collocation method to computing periodic solutions of coupled renewal and delay differential equations. Through a rigorous error analysis, we prove convergence of the relevant finite-element method and…

Numerical Analysis · Mathematics 2023-05-23 Alessia andò , Dimitri Breda

We introduce generalised finite difference methods for solving fully nonlinear elliptic partial differential equations. Methods are based on piecewise Cartesian meshes augmented by additional points along the boundary. This allows for…

Numerical Analysis · Mathematics 2017-06-26 Brittany D. Froese , Tiago Salvador

We present a multiscale finite element method for a diffusion problem with rough and high contrast coefficients. The construction of the multiscale finite element space is based on the localized orthogonal decomposition methodology and it…

Numerical Analysis · Mathematics 2025-11-11 Susanne C. Brenner , José C. Garay , Li-yeng Sung

Partial differential equations (PDE) often involve parameters, such as viscosity or density. An analysis of the PDE may involve considering a large range of parameter values, as occurs in uncertainty quantification, control and…

Numerical Analysis · Mathematics 2017-09-28 Max Gunzburger , Nan Jiang , Michael Schneier

The Multiquadric Radial Basis Function (MQ) Method is a meshless collocation method with global basis functions. It is known to have exponentional convergence for interpolation problems. We descretize nonlinear elliptic PDEs by the MQ…

Numerical Analysis · Mathematics 2025-10-20 A. I. Fedoseyev , M. J. Friedman , E. J. Kansa

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional reduced order models (ROMs) - built, e.g., through proper orthogonal decomposition (POD) - when applied to…

Numerical Analysis · Mathematics 2021-11-03 Stefania Fresca , Andrea Manzoni

In this paper, we develop a gradient recovery based linear (GRBL) finite element method (FEM) and a Hessian recovery based linear (HRBL) FEM for second order elliptic equations in non-divergence form. The elliptic equation is casted into a…

Numerical Analysis · Mathematics 2022-11-09 Minqiang Xu , Runchang Lin , Qingsong Zou

We consider approximations to the solutions of differential Riccati equations in the context of linear quadratic regulator problems, where the state equation is governed by a multiscale operator. Similarly to elliptic and parabolic…

Numerical Analysis · Mathematics 2018-08-14 Axel Målqvist , Anna Persson , Tony Stillfjord

We present an efficient method for the computation of homogenized coefficients of divergence-form operators with random coefficients. The approach is based on a multiscale representation of the homogenized coefficients. We then implement…

Numerical Analysis · Mathematics 2019-05-17 A. Hannukainen , J. -C. Mourrat , H. Stoppels

We formulate and analyze a multiscale method for an elliptic problem with an oscillatory coefficient based on a skeletal (hybrid) formulation. More precisely, we employ hybrid discontinuous Galerkin approaches and combine them with the…

Numerical Analysis · Mathematics 2025-02-03 Peipei Lu , Roland Maier , Andreas Rupp

Partial differential equation (PDE) models with multiple temporal/spatial scales are prevalent in several disciplines such as physics, engineering, and many others. These models are of great practical importance but notoriously difficult to…

Numerical Analysis · Mathematics 2023-04-17 Junpeng Hu , Shi Jin , Lei Zhang

We consider a finite element method for elliptic equation with heterogeneous and possibly high-contrast coefficients based on primal hybrid formulation. A space decomposition as in FETI and BDCC allows a sequential computations of the…

Numerical Analysis · Mathematics 2024-04-29 Alexandre L. Madureira , Marcus Sarkis

We derive optimal-order homogenization rates for random nonlinear elliptic PDEs with monotone nonlinearity in the uniformly elliptic case. More precisely, for a random monotone operator on $\mathbb{R}^d$ with stationary law (i.e. spatially…

Analysis of PDEs · Mathematics 2021-01-01 Julian Fischer , Stefan Neukamm

In this paper, we introduce the proper latent decomposition (PLD) as a generalization of the proper orthogonal decomposition (POD) on manifolds. PLD is a nonlinear reduced-order modeling technique for compressing high-dimensional data into…

Machine Learning · Computer Science 2024-12-03 Daniel Kelshaw , Luca Magri
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