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The Intrinsic Surface Finite Element Method (ISFEM) was recently proposed to solve Partial Differential Equations (PDEs) on surfaces. ISFEM proceeds by writing the PDE with respect to a local coordinate system anchored to the surface and…

Numerical Analysis · Mathematics 2024-10-08 Elena Bachini , Mario Putti

This paper concerns with iterative schemes for the perfect reconstruction of functions belonging to multiresolution spaces on bounded manifolds from nonuniform sampling. The schemes have optimal complexity in the sense that the…

Numerical Analysis · Mathematics 2007-05-23 Massimo Fornasier , Laura Gori

In this paper, we present an Online Generalized Multiscale Finite Element Method(Online GMsFEM) for heat and mass transfer problem in heterogeneous media with artificial ground freezing pipes. The mathematical model of the process is based…

Numerical Analysis · Mathematics 2022-05-31 Denis Spiridonov , Sergei Stepanov , Vasil`ev Vasiliy

Gaussian process regression has recently emerged as a powerful, system-agnostic tool for building global potential energy surfaces (PES) of polyatomic molecules. While the accuracy of GP models of PES increases with the number of potential…

Chemical Physics · Physics 2019-07-23 Jun Dai , Roman V. Krems

Understanding Earth's subsurface is critical for energy transition, natural hazard mitigation, and planetary science. Yet subsurface analysis remains fragmented, with separate models required for structural interpretation, stratigraphic…

We present an embedding scheme for periodic systems that facilitates the treatment of the physically important part (here the unit cell) with advanced electronic-structure methods, that are computationally too expensive for periodic…

Materials Science · Physics 2016-04-08 Wael Chibani , Xinguo Ren , Matthias Scheffler , Patrick Rinke

We present a cut finite element method for the heat equation on two overlapping meshes: a stationary background mesh and an overlapping mesh that moves around inside/"on top" of it. Here the overlapping mesh is prescribed a simple…

Numerical Analysis · Mathematics 2023-03-02 Mats G. Larson , Anders Logg , Carl Lundholm

Composite function minimization captures a wide spectrum of applications in both computer vision and machine learning. It includes bound constrained optimization and cardinality regularized optimization as special cases. This paper proposes…

Optimization and Control · Mathematics 2016-12-08 Ganzhao Yuan , Wei-Shi Zheng , Bernard Ghanem

Despite their success, Segment Anything Models (SAMs) experience significant performance drops on severely degraded, low-quality images, limiting their effectiveness in real-world scenarios. To address this, we propose GleSAM, which…

Computer Vision and Pattern Recognition · Computer Science 2026-04-27 Guangqian Guo , Yong Guo , Xuehui Yu , Wenbo Li , Yaoxing Wang , Shan Gao

First principles calculations based on density functional theory are having an incerasing impact on our understanding of molecule-surface interactions. For example, calculations of the multi-dimensional potential energy surface have…

Materials Science · Physics 2007-05-23 O. Gulseren , D. M. Bird , S. E. Humphreys

An iterative method is derived for image reconstruction. Among other attributes, this method allows constraints unrelated to the radiation measurements to be incorporated into the reconstructed image. A comparison is made with the widely…

Computational Physics · Physics 2011-01-06 Clinton DeW. Van Siclen

In diffusion MRI (dMRI), a good sampling scheme is important for efficient acquisition and robust reconstruction. Diffusion weighted signal is normally acquired on single or multiple shells in q-space. Signal samples are typically…

Medical Physics · Physics 2017-09-26 Jian Cheng , Dinggang Shen , Pew-Thian Yap , Peter J. Basser

The determination of liquid phase equilibria plays an important role in chemical process simulation. This work presents a generalization of an approach called the convex envelope method (CEM), which constructs all liquid phase equilibria…

Chemical Physics · Physics 2023-06-01 Quirin Göttl , Jonathan Pirnay , Dominik G. Grimm , Jakob Burger

Given a graphical model (GM), computing its partition function is the most essential inference task, but it is computationally intractable in general. To address the issue, iterative approximation algorithms exploring certain local…

Machine Learning · Computer Science 2019-05-15 Sejun Park , Eunho Yang , Se-Young Yun , Jinwoo Shin

In this work, we extend the meshfree generalized multiscale exponential integration framework introduced in Nikiforov et al. (2025) to the simulation of three-dimensional advection--diffusion problems in heterogeneous and high-contrast…

In this paper, we discuss a general multiscale model reduction framework based on multiscale finite element methods. We give a brief overview of related multiscale methods. Due to page limitations, the overview focuses on a few related…

Numerical Analysis · Mathematics 2016-06-22 Eric Chung , Yalchin Efendiev , Thomas Y. Hou

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

The Boltzmann equation, as a model equation in statistical mechanics, is used to describe the statistical behavior of a large number of particles driven by the same physics laws. Depending on the media and the particles to be modeled, the…

Numerical Analysis · Mathematics 2019-04-16 Eric Chung , Yalchin Efendiev , Yanbo Li , Qin Li

We introduce a goal-oriented strategy for multiscale computations performed using the Multiscale Finite Element Method (MsFEM). In a previous work, we have shown how to use, in the MsFEM framework, the concept of Constitutive Relation Error…

Numerical Analysis · Mathematics 2019-08-02 Ludovic Chamoin , Frederic Legoll

We introduce the Generalized Energy Based Model (GEBM) for generative modelling. These models combine two trained components: a base distribution (generally an implicit model), which can learn the support of data with low intrinsic…

Machine Learning · Statistics 2021-12-22 Michael Arbel , Liang Zhou , Arthur Gretton