Development of Geometrically-Flexible Physics-Based Convolution Kernels

基于几何灵活物理的卷积核的开发

• 批准号: 2110745
• 负责人: Samy Wu Fung
• 金额: $ 29.76万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110745&HistoricalAwards=false
• 关键词: Development; Geometrically; Flexible; Physics; Based

Data compression is essential in many areas of technology such as satellite imaging, speech recognition, database design, and much more. However, most of this understanding is based on data with good properties.  Increasingly, there is a necessity for techniques that can be applied to complex or incomplete data, so-called flexible data. This project contributes to the development of more advanced compression techniques that can be applied to flexible data.  The main purpose of the project is the development of physics-based computational techniques that enhance data compression algorithms by making them more accurate and efficient.  The ideas developed in this project are applicable to more traditional computational fluid dynamics applications such as hypersonics and atmospheric modeling as well as areas such as machine learning.  In addition to the scientific impact, this project broadens the participation of women in the computational sciences.  It includes support for student mentorship, traineeship, and retention.   The computational skills that the students will obtain are broadly applicable and allows them access to a variety of career options, including in areas of great national need. The PI expects that the tools developed in this proposal will also be included in future outreach talks to the general public.The overall goal of this research is to develop innovative, mathematically rigorous, geometrically flexible, physics-based, multi-dimensional convolution kernels that are useful in areas of data compression, shock filtering, post-processing, and machine learning. The GEOCONKER (GEOmetrically-flexible physics-based CONvolution KERnels) project will not only concentrate on establishing a robust analytical framework, but also on the efficient implementation of these kernels. This will allow for enhancing accurate capturing of multi-scale physics that includes information from sensor data. These techniques will be able to be applied to different types of data and in different manners.   These convolution kernels will aid in establishing provable high-order resolution filters by establishing the interaction between the mathematics, physics, numerics, and geometry in applications.  These novel techniques will use (flexible) spline functions that can adapt to the geometry of the given data on the fly. This will allow for efficient computational codes that will enhance the accurate capturing and filtering of multi-scale physics.  This will include kernels that are useful in shock capturing, accuracy enhancement, and filtering of noisy data. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在许多技术领域（例如卫星成像，语音识别，数据库设计等）中，数据压缩至关重要。但是，大多数理解都是基于具有良好属性的数据。越来越多的技术有必要应用于复杂或不完整的数据，即所谓的灵活数据。该项目有助于开发更先进的压缩技术，这些技术可应用于灵活的数据。该项目的主要目的是开发基于物理的计算技术，该技术通过使它们更准确和高效来增强数据压缩算法。该项目中开发的想法适用于更传统的计算流体动力学应用，例如超为人物和大气建模以及机器学习等领域。除了科学影响外，该项目还扩大了妇女在计算科学中的参与。它包括支持学生指导，实习生和保留的支持。学生将获得的计算技能广泛适用，并允许他们访问各种职业选择，包括在国家需求巨大的领域。 PI预计，该提案中开发的工具也将包括在与公众的未来外展谈判中。这项研究的总体目标是开发创新的，数学上严格的，几何的基于物理，基于物理的，基于物理的，多维的卷积内核，这些核能可用于数据压力，冲击，震动过滤，后处理，后处理和机器学习。 Geoconker（基于几何富裕物理学的卷积内核）项目不仅将集中于建立强大的分析框架，而且还集中于这些内核的有效实施。这将允许增强精确捕获多尺度物理的捕获，其中包括传感器数据中的信息。这些技术将使用（灵活的）样条函数来适应给定数据的几何形状。这将允许有效的计算代码，以增强多尺度物理的准确捕获和过滤。这将包括可用于捕获冲击，准确性增强和噪声数据过滤的内核。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响审查标准来评估被认为是宝贵的支持。