Speaker
Nobuyuki Matsumoto
(Boston University)
Description
Riemannian manifold HMC (RMHMC), following the basic idea of Fourier acceleration, has been studied as a viable algorithm toward mitigating critical slowing down. In fact, by using multilevel integration, the algorithmic overhead becomes additive to the fermionic inversion cost that dominates in production runs, especially with domain-wall fermions. Given the rapid development of AI/ML, we explore constructing the kernel as a neural network. Using the CPN model as a testbed, we report on the results from representative kernels. We further report on software development towards migrating to QCD.
Authors
Ryan Abbott
(Columbia University)
Peter Boyle
Luchang Jin
(Univeristy of Connecticut)
Christopher Kelly
Christoph Lehner
(Universität Regensburg)
Nobuyuki Matsumoto
(Boston University)