Part I: Steady States in Two-Species Particle Aggregation. Part II: Sparse Representations for Multiscale PDE

Abstract

The first part of this dissertation combines continuum limits of nonlocally interacting particles with stability analysis of nonlinear PDE to analyze the steady states of systems of pairwise-interacting particles. Models employing these assumptions cover a cornucopia of physical systems, from insect swarms and bacterial colonies to nanoparticle self-assembly. In this joint work with Theodore Kolokolnikov and Andrea Bertozzi [60], we study a continuum model with densities supported on co-dimension one curves for two-species particle interaction in R2, and apply linear stability analysis of concentric ring steady states to characterize the steady state patterns and instabilities which form. Conditions for linear well-posedness are determined and these results are compared to simulations of the discrete particle dynamics, showing predictive power of the linear theory. Part II continues the work started in [76], which proposes the sparse Fourier domain approximation of solutions to multiscale PDE problems by soft thresholding. In this joint work with Hayden Schaeffer and Stanley Osher [61], we show that the method enjoys a number of desirable numerical and analytic properties, including convergence for linear PDE and a modified equation resulting from the sparse approximation. We also extend the method to solve elliptic equations and introduce sparse approximation of differential operators in the Fourier domain. The effectiveness of the method is demonstrated on homogenization examples where its complexity is dependent only on the sparsity of the problem and constant in many cases.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2015
Accession Number
ADA624731

Entities

People

  • Alan P. Mackey

Organizations

  • University of California, Los Angeles

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Compressed Sensing
  • Computational Fluid Dynamics
  • Computational Science
  • Data Mining
  • Differential Equations
  • Dynamics
  • Equations
  • Information Science
  • Inverse Problems
  • Machine Learning
  • Mathematics
  • Partial Differential Equations
  • Simulations
  • Steady State
  • Supervised Machine Learning
  • Theorems
  • Two Dimensional

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Neural Network Machine Learning.

Technology Areas

  • Biotechnology