Variational Problems: From Foundational Questions to Direct Applications in Image Processing and Materials Science

变分问题：从基础问题到图像处理和材料科学中的直接应用

• 批准号: RGPIN-2020-04911
• 负责人: Choksi, Rustum
• 金额: $ 2.7万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717460
• 关键词: Variational; Problems; Foundational; Questions; Direct

Variational problems permeate diverse scientific disciplines; from pure mathematics to applied topics in image processing and materials science. This proposal addresses four (classes of) variational problems and can be separated into two parts. The first part is more foundational from the mathematical point of view and involves two classical, non-local and non-convex variational problems: optimal quantization (or optimal centrodial Voronoi tessellations) and Gamow's Liquid Drop Model (and its anisotropic variants). For both problems, we will focus on two central goals: (a) to what extent can one rigorously characterize ground states (global minimizers); (b) can one develop numerical algorithms to probe the energy landscape and find low energy states. The second part of the proposal is highly application driven, and pertains to addressing and developing specific novel variational problems in two very different fields: (i) deblurring and denoising in image processing and (ii) grain growth in polycrystalline materials. For image processing, we will develop on a recent and remarkably successful Bayesian variational method, introduced by the applicant et al, which is based upon the idea of maximum entropy on the mean via the probability distribution of the image. For grain growth, we address phase field crystal models and their gradient flow dynamics. Our focus lies on: the understanding of universal statistical distributions for geometric metrics during grain growth; and addressing various extensions and reductions of phase field crystal models with an emphasis on the connection between mathematical and desirable physical properties. Throughout this proposal, we will employ techniques (new and old) which span tools from the modern calculus of variations, numerical analysis, convex optimization, and mathematical statistics.

变分问题渗透到各个科学学科中；从纯数学到应用主题在图像处理和材料科学。该方案解决了四（类）变分问题，可分为两部分。第一部分从数学的角度来看是更基础的，涉及两个经典的，非局部和非凸变分问题：最优量化（或最优中心Voronoi镶嵌）和Gamow的液滴模型（及其各向异性变体）。对于这两个问题，我们将关注两个中心目标：(a)在多大程度上可以严格表征基态（全局最小化）；(b)是否可以开发数值算法来探测能量格局并找到低能态。