The DnC2s project has successfully applied reinforcement learning with uncertainty quantification to the Fermilab gradient magnet power supply in the Fermilab Booster. This data-driven machine-learning-based surrogate model highlights the importance of incorporating uncertainty quantification, particularly for out-of-distribution uncertainties. These uncertainties need to be well-calibrated and understood to prevent reinforcement learning algorithms from exploring state and action areas that are poorly represented in the available data. In future endeavors, the project aims to extend this application to other facilities such as the Jefferson Lab Continuous Electron Beam Accelerator Facility and SLAC National Accelerator Laboratory.
Partnership:
- Oak Ridge National Laboratory (https://www.ornl.gov/)
- Pacific Northwest National Laboratory (https://www.pnnl.gov/)
- University of California, Santa Barbara (https://www.ucsb.edu/)
- Arizona State University (https://www.asu.edu/)
Project Link:
Related Publications:
The SciDAC Quantom project aims to extract a quark and gluon tomography of nuclei and answer important questions on the nature of visible matter at the femtoscale. The project brings together Jefferson Lab with Argonne National Lab, Virginia Tech, and Old Dominion University to develop a common and modular framework of core base classes and workflows to generate an event-level quantum correlation function inference framework. Horovod is utilized to distribute the model across many GPUs in parallel.
Partnership:
- Argonne National Laboratory (https://www.anl.gov/)
- Virginia Tech (https://www.vt.edu/)
- Old Dominion University (https://www.odu.edu/)
Related Publications:
As the second most populous city in the Hampton Roads region with a total population of over 1.7 million, Norfolk is home to a diverse population and is a major economic and cultural center. Additionally, the site of the world’s largest naval base, the deepest water harbor on the US East Coast, and a NATO headquarters. Norfolk is particularly vulnerable to coastal flooding, climate change will increase the frequency and severity of flooding events, and event characteristics will diverge from what we see today. Data-driven techniques to predict urban flooding must produce well-calibrated prediction uncertainties that account for our changing climate.
Jefferson Lab's Data Science department's research into uncertainty quantification for machine learning methods combined and our partnerships with Old Dominion University and the Hampton Roads Biomedical Research Consortium allow us to investigate the critical issue of predicting urban pluvial flooding using machine learning surrogate models.
Partnership:
- Old Dominion University (https://www.odu.edu/)
Related Publications:
Spallation Neutron Source at ORNL
Laboratory Directed Research and Development
Experimental Hall
