Collaborative Research: Inference and Uncertainty Quantification for High Dimensional Systems in Remote Sensing: Methods, Computation, and Applications

合作研究：遥感高维系统的推理和不确定性量化：方法、计算和应用

基本信息

批准号：
2053668
负责人：
Emily Kang
金额：
$ 15.99万
依托单位：
University of Cincinnati Main Campus
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053668&HistoricalAwards=false
关键词：
Collaborative Research Inference Uncertainty Quantification

项目摘要

Complex mathematical models are ubiquitous in physical, atmospheric, biological, and engineering sciences. These models, often called simulators, are used to describe complicated interactions among many variables and processes in the systems and are sometimes accompanied by massive data. The process of extracting information and knowledge from the simulators and observational data can be called an inverse problem. However, solving inverse problems and quantifying the uncertainty is challenging. This project addresses these challenges with novel methods, efficient algorithms, and software tools to enable fast simulations and inverse problem solutions. A particular application in this project is inverse problems in remote sensing. This research project integrates the advancements in statistics, applied mathematics, data science, and remote sensing. It will provide ways to assess the quality and uncertainty of remote sensing data products to address scientific hypotheses. The PIs will apply and evaluate these new methods in the context of inverse problems in remote sensing for carbon monitoring, but these methods can also be used for data-intensive inverse problems in many other areas including climatology, geophysics, and medical imaging. This project will directly train student researchers and will develop educational materials. The project findings will be shared via journal publications and conference presentations.This collaborative research project will contribute to significant advances in statistical modeling, uncertainty quantification, and efficient scalable methods to solve large-scale inverse problems associated with high-dimensional systems. The PIs will establish new methods to build statistical emulators with computational efficiency and statistical guarantees. The scalability is achieved by joint dimension reduction for both the input and output spaces, while theoretical approximation properties of the resulting emulators will be derived. The resulting emulators will facilitate large-scale simulation-based uncertainty quantification experiments for remote sensing data. This framework of statistical emulation will also be integrated into the algorithms to infer inverse problem solutions to enable faster computation. With a particular focus on high-dimensional systems encountered in remote sensing, the methods developed will lead to a new paradigm of statistical methods for complex inference problems and uncertainty quantification in remote sensing and transform the current practice of remote sensing retrieval. Open-source software for the proposed new approaches will be made available to a wide community of scientists and engineers. By partnering with collaborators in remote sensing, the methods developed in this project will be of practical utility for researchers in various applications including carbon monitoring.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.

复杂的数学模型在物理，大气，生物学和工程科学中无处不在。这些模型（通常称为模拟器）用于描述系统中许多变量和过程之间的复杂相互作用，有时伴随着大量数据。从模拟器中提取信息和知识的过程以及观察数据可以称为反问题。但是，解决反问题并量化不确定性是具有挑战性的。该项目通过新颖的方法，有效的算法和软件工具来解决这些挑战，以实现快速模拟和逆问题解决方案。该项目中的特定应用是遥感中的反问题。该研究项目整合了统计，应用数学，数据科学和遥感方面的进步。它将提供评估遥感数据产品的质量和不确定性以解决科学假设的方法。 PI将在碳监测的遥感中应用和评估这些新方法，但这些方法也可以用于许多其他领域的数据密集型反问题，包括气候，地球物理学和医学成像。该项目将直接培训学生研究人员，并将开发教育材料。该项目的发现将通过期刊出版物和会议演讲共享。该协作研究项目将在统计建模，不确定性量化和有效的可扩展方法方面取得重大进展，以解决与高维系统相关的大规模逆问题。 PI将建立新的方法来构建具有计算效率和统计保证的统计模拟器。通过降低输入空间和输出空间的关节维度可以实现可伸缩性，而所得模拟器的理论近似特性将得出。所得的仿真器将促进遥感数据的大规模模拟不确定性量化实验。该统计模拟的框架也将集成到算法中，以推断逆问题解决方案以实现更快的计算。特别关注遥感中遇到的高维系统，开发的方法将为复杂的推理问题和遥感中的不确定性量化提供新的统计方法范式，并改变了当前的遥感检索实践。拟议的新方法的开源软件将提供给广泛的科学家和工程师社区。通过与遥感方面的合作者合作，该项目中开发的方法对于包括碳监测在内的各种应用程序的研究人员将是实用实用性。该奖项反映了NSF的法定任务，并且认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持。