AI/ML for Applications in High Energy adn Nuclear Physics
Description
Develop state-of-the-art cycle-consistent GANs to bridge the gap between simulations and experimental data; develop real-time particle tracking with deep learning on field programmable gate arrays; explore the challenges of deploying ML modeling onto
Detailed example
Develop state-of-the-art cycle-consistent GANs to bridge the gap between simulations and experimental data; develop real-time particle tracking with deep learning on field programmable gate arrays; explore the challenges of deploying ML modeling onto real-time inference hardware - for High Energy or Nuclear Physics
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Develop state-of-the-art cycle-consistent GANs to bridge the gap between simulations and experimental data; develop real-time particle tracking with deep learning on field programmable gate arrays; explore the challenges of deploying ML modeling onto real-time inference hardware - for High Energy or Nuclear Physics
Audit / financial statement impact
Does not meet definition
Controls / human review
ATO: Not reported; PIA: Not published