I-Corps: Fast and Accurate Artificial Intelligence/Machine Learning Solutions to Inverse and Imaging Problems

I-Corps：针对逆向和成像问题的快速准确的人工智能/机器学习解决方案

• 批准号: 2224299
• 负责人: Tan Bui-Thanh
• 金额: $ 5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2224299&HistoricalAwards=false
• 关键词: Corps; Fast; Accurate; Artificial; Intelligence

The broader impact/commercial potential of this I-Corps project is the development of machine learning-enabled software to provide solutions to inverse and imaging problems with quantified uncertainty. The proposed technology may have application in the discovery of the location of oil and gas reservoirs, the discovery of the location of geothermal hotspots and seismic faults for harvesting clean geothermal energy, the discovery of the location of critical and rare mineral deposits for extraction, and the imaging of the interior of the human body for medical diagnosis. The goal is to reduce operational cost through the use of the proposed software platform; e.g., oil companies (or companies providing services to oil companies) may deploy this software to monitor, control, and make critical decisions in real time. The prediction and uncertainty quantification (UQ) capabilities of the software potentially may assist in decision making.This I-Corps project is based on the development of two advanced algorithms: (1) model-constrained machine learning methods to obtain real-time inverse/inference solutions with quantified uncertainty and (2) model-constrained methods to obtain real-time forward solutions with quantified uncertainty. The proposed technology brings together advances from stochastic programming, probability theory, applied mathematics, and machine learning. The proposed software is designed to provide both forward and inverse capabilities. In either mode, it can use training data and the governing equations to produce surrogate models, i.e., trained deep neural networks that respect both data and governing equations. The surrogate models may provide accurate predictions and associated uncertainties in real time for new scenarios. In addition, the training data may be customized according to the user needs. For example, oil companies (or companies providing services to oil companies) may deploy the proposed software to monitor, control, and make critical decisions in real time. These capabilities reduce the required human power, time, and finances, while eliminating development and maintenance costs and reducing update and operational costs.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.

这个I-Corps项目更广泛的影响/商业潜力是开发支持机器学习的软件，为具有量化不确定性的逆向和成像问题提供解决方案。所提出的技术可应用于发现石油和天然气储藏的位置，发现地热热点和地震断层的位置以收集清洁地热能，发现关键和稀有矿藏的位置以供开采，以及人体内部成像以供医疗诊断。 目标是通过使用建议的软件平台降低运营成本;例如，石油公司（或向石油公司提供服务的公司）可以部署该软件以真实的实时地监视、控制和做出关键决策。 该软件的预测和不确定性量化（UQ）功能可能有助于决策制定。该I-Corps项目基于两种先进算法的开发：（1）模型约束机器学习方法，以获得具有量化不确定性的实时逆/推理解决方案;（2）模型约束方法，以获得具有量化不确定性的实时正解。所提出的技术汇集了随机规划，概率论，应用数学和机器学习的进步。所提出的软件被设计为提供正向和反向能力。在任一模式中，它可以使用训练数据和控制方程来产生代理模型，即，经过训练的深度神经网络，既尊重数据又尊重控制方程。代理模型可以为新的场景提供真实的准确预测和相关联的不确定性。此外，训练数据可以根据用户需求定制。例如，石油公司（或向石油公司提供服务的公司）可以部署所提出的软件以真实的实时地监视、控制和做出关键决策。这些能力减少了所需的人力、时间和资金，同时消除了开发和维护成本，降低了更新和运营成本。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。