Artificial intelligence (AI) is a computer-based approaches to activities that normally require intelligence (translating data and information into knowledge) to perform effectively. Machine Learning is a form of artificial intelligence in which computer algorithms learn from data to form predictive models.
AI and ML are helping INL scientists pursue advances in engineering and energy research. Computers that mimic cognitive functions and apply complex algorithms to analyze data can help researchers solve a variety of technical issues. This new approach aids in everything from materials design for advanced reactors to making nuclear power plant control rooms more usable and efficient.
Our vision is to use AI and ML to glean new research insights and enhance INL’s core research capabilities.
INL’s vision is to change the world’s energy future and secure our nation’s critical infrastructure. To support INL’s research missions, we have five goals.
Artificial intelligence and machine learning support a variety of research areas and useful applications:
INL is working to raise awareness of its work in AI/ML and encourage more researchers to use the available resources. We are making our research outcomes available through technical reports and a series of symposia focusing on how AI/ML is impacting science and engineering. The presentations focus on the latest advances in AI/ML, current applications in the nuclear industry, research opportunities and ongoing collaborations.
INL AI/ML Symposium 9.0 – held Sept. 8, 2022
The 9.0 Symposium focused on natural language processing methods and applications.
INL AI/ML Symposium 8.0 – held May 26, 2022
The 8.0 symposium focused on computation infrastructure in artificial intelligence and machine learning.
INL AI/ML Symposium 7.0 – held February 10, 2022
The 7.0 Symposium focused on addressing data issues and using data for different science and engineering applications.
INL AI/ML Symposium 6.0 – held Oct. 14, 2021
The 6.0 Symposium focused on resilience, both in resilience applications of AI/ML or in resilience in AI/ML approaches.
INL AI/ML Symposium 5.0 – held June 8, 2021
The 5.0 Symposium focused on trustworthy AI/ML.
INL AI/ML Symposium. 4.0 – held February 9, 2021
The 4.0 Symposium focused on trustworthy AI/ML.
INL AI/ML Symposium 3.0 – held October 16, 2020
The 3.0 Symposium discussed how ML and AI are currently being applied in the industry, including opportunities for engagement and collaboration.
INL AI/ML Symposium 2.0 – held July 9, 2020
The 2.0 Symposium discussed how ML and AI are currently being applied in the industry, including opportunities for engagement and collaboration.
INL AI/ML Symposium 1.0 – held April 17, 2020
INL held the first symposium on AI/ML approaches and activities related to science and engineering in April. The “1.0 Symposium” focused on internal-to-INL activities and capabilities. A total of eleven speakers discussed a variety of current topics and future applications. Over 200 INL staff participated in the symposium.
Digital Innovation Center of Excellence
The Digital Innovation Center of Excellence at INL was created to support the development of advanced reactors through digital engineering. Developed in part to predict reactor performance for the Versatile Test Reactor, the Digital Innovation Center of Excellence is organized into eight key pillars:
For more information on DICE, click here.
Nuclear Safety and Regulatory Research Division
INL’s Nuclear Safety and Regulatory Research Division supports a wide variety of activities. In particular, it ensures the nation’s safe, competitive and sustainable use of engineered systems in many domains by applying INL capabilities to impactful issues in risk, reliability and operational performance.
Our recent AI/ML projects include:
To work with us on your AI/ML project or to find out more about these INL AI/ML activities, contact our subject matter experts:
Curtis Smith – Division Director, Nuclear Safety & Regulatory Research
Shad Staples – Manager, Data Analytics Lead and Visualization
Chris Ritter – Director, Digital Innovation Center of Excellence
Nancy Lybeck – Manager, Instrument Controls & Data Science
Craig Primer – Instrument Controls & Data Science
Ronald Boring – Human Factors & Reliability