Parallel-in-time integration (PinT) algorithms, such as Parareal, PFASST, or MGRIT, have demonstrated significant prowess in addressing time-dependent problems. However, the construction of coarse level models for these algorithms is a challenge, demanding expertise numerics, domain science, and a profound understanding of high-performance computing (HPC). The research done in this study is focussing on advancements in leveraging machine learning (ML) techniques, specifically neural operators (NOs), to offer a more generic and efficient approach to the development … Continue reading NeuralPint