Data-driven time-parallelism accepted into SISC

Data-driven coarse propagation to accelerate convergence

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.

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Kevin T. Carlberg
Machine Learning Research Scientist

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.