MLMC-PinT4Data: Parallel-in-time micro-macro Monte Carlo methods for uncertainty quantification and data assimilation KU Leuven
With the exponential increase of computational power, numerical simulation has become a routine tool in many scientific domains. However, the shift towards massive parallelism poses numerous challenges for algorithm design: parallelization must be considered from the outset of algorithm design. Recent successes have established the potential of parallel-in-time (PinT) integration as a powerful algorithmic paradigm, in addition to other forms ...