Assistant Professor Simon Mak is the Duke PI on an interdisciplinary grant funded by the Department of Energy, on developing new Bayesian uncertainty quantification methods for advancing important directions in nuclear physics and high-energy physics. This is a $1.3 million multi-institutional collaborative grant with theoretical and experimental physicists from Lawrence Berkeley National Laboratory, UC Berkeley and Wayne State University.
The study of complex nuclear phenomena, such as neutrinos and the quark-gluon plasma, is vital for understanding the intricate interactions between subatomic particles that arose in the early Universe. These studies involve a challenging "inverse" problem, where precise experimental measurements from particle detectors are used to learn parameters of interest via complex theoretical physics models.
Mak is the Duke PI on this interdisciplinary grant, and will develop new Bayesian uncertainty quantification methods, theory and algorithms for furthering such studies in modern nuclear science problems. A key bottleneck is the computationally intensive nature of the underlying theoretical physics models, which requires thousands of CPU hours to simulate for a single parameter setting. Mak's group will develop probabilistic machine learning and uncertainty quantification methods that greatly accelerate this expensive process, which will play an important role in advancing new nuclear science discoveries.