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

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.