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Harnessing billions of tasks for a scalable portable hydrodynamic simulation of the merger of two stars., , , , , , , , , and 5 other author(s). Int. J. High Perform. Comput. Appl., (2019)Surrogate-data-enriched Physics-Aware Neural Networks., , , and . CoRR, (2021)Octo-Tiger's New Hydro Module and Performance Using HPX+CUDA on ORNL's Summit., , , , , , , and . CoRR, (2021)Surrogate-data-enriched Physics-Aware Neural Networks., , , and . NLDL, Septentrio Academic Publishing, (2022)EXAHD: A Massively Parallel Fault Tolerant Sparse Grid Approach for High-Dimensional Turbulent Plasma Simulations., , , , , , , , and . Software for Exascale Computing, volume 136 of Lecture Notes in Computational Science and Engineering, Springer, (2020)PLSSVM: A (multi-)GPGPU-accelerated Least Squares Support Vector Machine., , and . IPDPS Workshops, page 818-827. IEEE, (2022)From piz daint to the stars: simulation of stellar mergers using high-level abstractions., , , , , , , , , and . SC, page 62:1-62:37. ACM, (2019)B-splines on sparse grids for surrogates in uncertainty quantification., , and . Reliab. Eng. Syst. Saf., (2021)Self-Learning Tuning for Post-Silicon Validation., , , and . CoRR, (2021)From Merging Frameworks to Merging Stars: Experiences using HPX, Kokkos and SIMD Types., , , , and . ESPM2@SC, page 10-19. IEEE, (2022)