8 papers · 1 filter
Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor
Vamika Rathi, Fatima Sehar, Finn Sommer +4
Lagrangian sensors have shown promise to improve operator awareness of conditions inside a chemical reactor but three-dimensional tracking remains a mostly unsolved challenge. We e…
Spectral Deferred Corrections in the framework of Runge-Kutta methods
Eugen Bronasco, Joscha Fregin, Daniel Ruprecht +1
We interpret a wide range of flavors of Spectral Deferred Corrections (SDC) as Runge-Kutta methods (RKM). Using Butcher series, we show that the considered class of SDC methods ach…
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…
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…
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…