Data-driven time-parallelism accepted into SISC
Data-driven coarse propagation to accelerate convergence
My paper with Lukas Brencher , Bernard Haasdonk , and Andrea Barth has been accepted in the SIAM Journal on Scientific Computing .
Kevin T. Carlberg
Affiliate Associate Professor of Applied Mathematics and Mechanical Engineering
My research combines machine learning, computational physics, and high-performance computing. The objective is to discover structure in data to drastically reduce the cost of simulating nonlinear dynamical systems at extreme scale. I also work on technologies that enable the future of virtual and augmented reality.