Literature DB >> 24170861

Compressed modes for variational problems in mathematics and physics.

Vidvuds Ozolins1, Rongjie Lai, Russel Caflisch, Stanley Osher.   

Abstract

This article describes a general formalism for obtaining spatially localized ("sparse") solutions to a class of problems in mathematical physics, which can be recast as variational optimization problems, such as the important case of Schrödinger's equation in quantum mechanics. Sparsity is achieved by adding an regularization term to the variational principle, which is shown to yield solutions with compact support ("compressed modes"). Linear combinations of these modes approximate the eigenvalue spectrum and eigenfunctions in a systematically improvable manner, and the localization properties of compressed modes make them an attractive choice for use with efficient numerical algorithms that scale linearly with the problem size.

Mesh:

Year:  2013        PMID: 24170861      PMCID: PMC3831964          DOI: 10.1073/pnas.1318679110

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  6 in total

1.  Nearsightedness of electronic matter.

Authors:  E Prodan; W Kohn
Journal:  Proc Natl Acad Sci U S A       Date:  2005-08-08       Impact factor: 11.205

2.  Optimally sparse representation in general (nonorthogonal) dictionaries via l minimization.

Authors:  David L Donoho; Michael Elad
Journal:  Proc Natl Acad Sci U S A       Date:  2003-02-21       Impact factor: 11.205

3.  Localized bases of eigensubspaces and operator compression.

Authors:  Weinan E; Tiejun Li; Jianfeng Lu
Journal:  Proc Natl Acad Sci U S A       Date:  2010-01-04       Impact factor: 11.205

4.  Sparse dynamics for partial differential equations.

Authors:  Hayden Schaeffer; Russel Caflisch; Cory D Hauck; Stanley Osher
Journal:  Proc Natl Acad Sci U S A       Date:  2013-03-26       Impact factor: 11.205

5.  Compressed plane waves yield a compactly supported multiresolution basis for the Laplace operator.

Authors:  Vidvuds Ozoliņš; Rongjie Lai; Russel Caflisch; Stanley Osher
Journal:  Proc Natl Acad Sci U S A       Date:  2014-01-21       Impact factor: 11.205

6.  Sparse principal component analysis by choice of norm.

Authors:  Xin Qi; Ruiyan Luo; Hongyu Zhao
Journal:  J Multivar Anal       Date:  2012-07-16       Impact factor: 1.473

  6 in total
  7 in total

1.  Learning partial differential equations via data discovery and sparse optimization.

Authors:  Hayden Schaeffer
Journal:  Proc Math Phys Eng Sci       Date:  2017-01       Impact factor: 2.704

2.  Compressed plane waves yield a compactly supported multiresolution basis for the Laplace operator.

Authors:  Vidvuds Ozoliņš; Rongjie Lai; Russel Caflisch; Stanley Osher
Journal:  Proc Natl Acad Sci U S A       Date:  2014-01-21       Impact factor: 11.205

3.  Discovering governing equations from data by sparse identification of nonlinear dynamical systems.

Authors:  Steven L Brunton; Joshua L Proctor; J Nathan Kutz
Journal:  Proc Natl Acad Sci U S A       Date:  2016-03-28       Impact factor: 11.205

4.  Discovery of nonlinear dynamical systems using a Runge-Kutta inspired dictionary-based sparse regression approach.

Authors:  Pawan Goyal; Peter Benner
Journal:  Proc Math Phys Eng Sci       Date:  2022-06-22       Impact factor: 3.213

5.  Koopman Invariant Subspaces and Finite Linear Representations of Nonlinear Dynamical Systems for Control.

Authors:  Steven L Brunton; Bingni W Brunton; Joshua L Proctor; J Nathan Kutz
Journal:  PLoS One       Date:  2016-02-26       Impact factor: 3.240

6.  Data-driven discovery of partial differential equations.

Authors:  Samuel H Rudy; Steven L Brunton; Joshua L Proctor; J Nathan Kutz
Journal:  Sci Adv       Date:  2017-04-26       Impact factor: 14.136

7.  Sparsifying priors for Bayesian uncertainty quantification in model discovery.

Authors:  Seth M Hirsh; David A Barajas-Solano; J Nathan Kutz
Journal:  R Soc Open Sci       Date:  2022-02-23       Impact factor: 2.963

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.