2 citations · 2 across the 6 of their papers we have counts for
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Enforcing boundary conditions for physics-informed neural operators
Niklas Göschel, Sebastian Götschel, Daniel Ruprecht
Machine-learning based methods like physics-informed neural networks and physics-informed neural operators are becoming increasingly adept at solving even complex systems of partia…
Impact of spatial coarsening on Parareal convergence for the linear advection equation
Judith Angel, Sebastian Götschel, Daniel Ruprecht
The Parareal parallel-in-time integration method often performs poorly when applied to hyperbolic partial differential equations. This effect is even more pronounced when the coars…
Relevance of the Basset history term for Lagrangian particle dynamics
Julio Urizarna-Carasa, Daniel Ruprecht, Alexandra von Kameke +1
The movement of small but finite spherical particles in a fluid can be described by the Maxey-Riley equation (MRE) if they are too large to be considered passive tracers. The MRE c…
Fast-wave slow-wave spectral deferred correction methods applied to the compressible Euler equations
Alex Brown, Joscha Fregin, Thomas Bendall +3
This paper investigates the application of a fast-wave slow-wave spectral deferred correction time-stepping method (FWSW-SDC) to the compressible Euler equations. The resulting mod…
Resilience Against Soft Faults through Adaptivity in Spectral Deferred Correction
Thomas Saupe, Sebastian Götschel, Thibaut Lunet +2
As supercomputers grow in hardware complexity, their susceptibility to faults increases and measures need to be taken to ensure the correctness of results. Some numerical algorithm…
Space-time parallel scaling of Parareal with a physics-informed Fourier Neural Operator coarse propagator applied to the Black-Scholes equation
Abdul Qadir Ibrahim, Sebastian Götschel, Daniel Ruprecht
Iterative parallel-in-time algorithms like Parareal can extend scaling beyond the saturation of purely spatial parallelization when solving initial value problems. However, they re…