An asymptotic parallel-in-time method for highly oscillatory PDEs

© 2014, Society for Industrial and Applied Mathematics. Available online at http://epubs.siam.org/doi/abs/10.1137/130914577We present a new time-stepping algorithm for nonlinear PDEs that exhibit scale separation in time. Our scheme combines asymptotic techniques (which are inexpensive but can have insufficient accuracy) with parallel-in-time methods (which, alone, can be inefficient for equations that exhibit rapid temporal oscillations). In particular, we use an asymptotic numerical method for computing, in serial, a solution with low accuracy, and a more expensive fine solver for iteratively refining the solutions in parallel. We present examples on the rotating shallow water equations that demonstrate that significant parallel speedup and high accuracy are achievable

Stabilization of parareal algorithms for long time computation of a class of highly oscillatory Hamiltonian flows using data

Applying parallel-in-time algorithms to multiscale Hamiltonian systems to obtain stable long time simulations is very challenging. In this paper, we present novel data-driven methods aimed at improving the standard parareal algorithm developed by Lion, Maday, and Turinici in 2001, for multiscale Hamiltonian systems. The first method involves constructing a correction operator to improve a given inaccurate coarse solver through solving a Procrustes problem using data collected online along parareal trajectories. The second method involves constructing an efficient, high-fidelity solver by a neural network trained with offline generated data. For the second method, we address the issues of effective data generation and proper loss function design based on the Hamiltonian function. We show proof-of-concept by applying the proposed methods to a Fermi-Pasta-Ulum (FPU) problem. The numerical results demonstrate that the Procrustes parareal method is able to produce solutions that are more stable in energy compared to the standard parareal. The neural network solver can achieve comparable or better runtime performance compared to numerical solvers of similar accuracy. When combined with the standard parareal algorithm, the improved neural network solutions are slightly more stable in energy than the improved numerical coarse solutions

Components of Nonlinear Oscillation and Optimal Averaging for Stiff PDEs

A novel solver which uses finite wave averaging to mitigate oscillatory stiffness is proposed and analysed. We have found that triad resonances contribute to the oscillatory stiffness of the problem and that they provide a natural way of understanding stability limits and the role averaging has on reducing stiffness. In particular, an explicit formulation of the nonlinearity gives rise to a stiffness regulator function which allows for analysis of the wave averaging. A practical application of such a solver is also presented. As this method provides large timesteps at comparable computational cost but with some additional error when compared to a full solution, it is a natural choice for the coarse solver in a Parareal-style parallel-in-time method

Parareal Convergence for Oscillatory PDEs with Finite Time-scale Separation

This is the final version. Available on open access from the Society for Industrial and Applied Mathematics via the DOI in this recordA variant of the Parareal method for highly oscillatory systems of PDEs was proposed by Haut and Wingate (2014). In that work they proved superlinear conver- gence of the method in the limit of infinite time scale separation. Their coarse solver features a coordinate transformation and a fast-wave averag- ing method inspired by analysis of multiple scales PDEs and is integrated using an HMM-type method. However, for many physical applications the timescale separation is finite, not infinite. In this paper we prove con- vergence for finite timescale separaration by extending the error bound on the coarse propagator to this case. We show that convergence requires the solution of an optimization problem that involves the averaging win- dow interval, the time step, and the parameters in the problem. We also propose a method for choosing the averaging window relative to the time step based as a function of the finite frequencies inherent in the problem.University of Exete