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In this paper, we consider the fundamental problem of approximation of functions on a low-dimensional manifold embedded in a high-dimensional space, with noise affecting both in the data and values of the functions. Due to the curse of…

Numerical Analysis · Mathematics 2020-12-29 Shira Faigenbaum-Golovin , David Levin

In this paper, we present a study on how to develop an efficient multiscale simulation strategy for the dynamics of chemically active systems on low-dimensional supports. Such reactions are encountered in a wide variety of situations,…

Computational Physics · Physics 2015-06-04 Giacomo Mazzi , Yannick De Decker , Giovanni Samaey

The ability to predict patient-specific soft tissue deformations is key for computer-integrated surgery systems and the core enabling technology for a new era of personalized medicine. Element-Free Galerkin (EFG) methods are better suited…

Computational Engineering, Finance, and Science · Computer Science 2019-06-14 Grand Joldes , George Bourantas , Benjamin Zwick , Habib Chowdhury , Adam Wittek , Sudip Agrawal , Konstantinos Mountris , Damon Hyde , Simon K. Warfield , Karol Miller

We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality. Existing formulations tackle this problem through heavy manifold optimization in the spectral domain, given…

Computer Vision and Pattern Recognition · Computer Science 2023-03-28 Souhaib Attaiki , Gautam Pai , Maks Ovsjanikov

An exact arithmetic, memory efficient direct solution method for finite element method (FEM) computations is outlined. Unlike conventional black-box or low-rank direct solvers that are opaque to the underlying physical problem, the proposed…

Computational Engineering, Finance, and Science · Computer Science 2020-02-13 Javad Moshfegh , Marinos N. Vouvakis

Lensless imaging is a popular research field for the advantages of small size, wide field-of-view and low aberration in recent years. However, some traditional lensless imaging methods suffer from slow convergence, mechanical errors and…

This paper studies adaptive first-order least-squares finite element methods for second-order elliptic partial differential equations in non-divergence form. Unlike the classical finite element method which uses weak formulations of PDEs…

Numerical Analysis · Mathematics 2019-06-28 Weifeng Qiu , Shun Zhang

Meshless methods approximate operators in a specific node as a weighted sum of values in its neighbours. Higher order approximations of derivatives provide more accurate solutions with better convergence characteristics, but they come at…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-09-12 Jon Vehovar , Miha Rot , Gregor Kosec

In this work, we employ the Constraint Energy Minimizing Generalized Multiscale Finite Element Method (CEM-GMsFEM) to solve the problem of linear heterogeneous poroelasticity with coefficients of high contrast. The proposed method makes use…

Numerical Analysis · Mathematics 2019-09-04 Shubin Fu , Robert Altmann , Eric T. Chung , Roland Maier , Daniel Peterseim , Sai-Mang Pun

We provide a concise review of the exponentially convergent multiscale finite element method (ExpMsFEM) for efficient model reduction of PDEs in heterogeneous media without scale separation and in high-frequency wave propagation. ExpMsFEM…

Numerical Analysis · Mathematics 2023-02-07 Yifan Chen , Thomas Y. Hou , Yixuan Wang

In this paper, attempt is made to solve a few problems using the Polynomial Point Collocation Method (PPCM), the Radial Point Collocation Method (RPCM), Smoothed Particle Hydrodynamics (SPH), and the Finite Point Method (FPM). A few…

Computational Engineering, Finance, and Science · Computer Science 2015-06-10 Kirana Kumara P

Deep learning and the collocation method are merged and used to solve partial differential equations describing structures' deformation. We have considered different types of materials: linear elasticity, hyperelasticity (neo-Hookean) with…

Machine Learning · Computer Science 2021-11-24 Diab W. Abueidda , Qiyue Lu , Seid Koric

Solving partial differential equations (PDEs) with machine learning typically requires training a new neural network for every new equation. This optimization is slow. We introduce MetaColloc. It is an optimization-free and data-free…

Machine Learning · Computer Science 2026-05-13 Zichuan Yang

We develop efficient and high-order accurate finite difference methods for elliptic partial differential equations in complex geometry in the Difference Potentials framework. The main novelty of the developed schemes is the use of local…

Numerical Analysis · Mathematics 2023-06-28 Qing Xia

In this paper, we address a way to reduce the total computational cost of meshless approximation by reducing the required stencil size through spatial variation of computational node regularity. Rather than covering the entire domain with…

Numerical Analysis · Mathematics 2024-04-04 Mitja Jančič , Miha Rot , Gregor Kosec

We develop a lensless compressive imaging architecture, which consists of an aperture assembly and a single sensor, without using any lens. An anytime algorithm is proposed to reconstruct images from the compressive measurements; the…

Computer Vision and Pattern Recognition · Computer Science 2015-08-17 Xin Yuan , Hong Jiang , Gang Huang , Paul Wilford

In this paper, we construct a combined multiscale finite element method (MsFEM) using the Local Orthogonal Decomposition (LOD) technique to solve the multiscale problems which may have singularities in some special portions of the…

Numerical Analysis · Mathematics 2022-09-14 Kuokuo Zhang , Weibing Deng , Haijun Wu

A common numerical task is to represent functions which are highly spatially anisotropic, and to solve differential equations related to these functions. One way such anisotropy arises is that information transfer along one spatial…

Numerical Analysis · Mathematics 2017-01-04 Ben F McMillan

We generalise the non-affine theory of viscoelasticity for use with large, well-sampled systems of arbitrary chemical complexity. Having in mind predictions of mechanical and vibrational properties of amorphous systems with atomistic…

Disordered Systems and Neural Networks · Physics 2020-07-27 Ivan Kriuchevskyi , Vladimir V. Palyulin , Rico Milkus , Robert M. Elder , Timothy W. Sirk , Alessio Zaccone

The aggregated unfitted finite element method (AgFEM) is a methodology recently introduced in order to address conditioning and stability problems associated with embedded, unfitted, or extended finite element methods. The method is based…

Numerical Analysis · Computer Science 2019-08-20 Francesc Verdugo , Alberto F. Martín , Santiago Badia