Data-Driven Time Parallelism via Forecasting

DOI10.1137/18M1174362OpenAlexW2964173708WikidataQ127826976 ScholiaQ127826976MaRDI QIDQ5230597

Andrea Barth, Bernard Haasdonk, Lukas Brencher, Kevin T. Carlberg

Publication date: 28 August 2019

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.09049

zbMATH Keywords

model reductionforecastingpararealtime-parallelgappy proper orthogonal decompositiondata-driven approximation

Mathematics Subject Classification ID

Stability and convergence of numerical methods for initial value and initial-boundary value problems involving PDEs (65M12) Stability and convergence of numerical methods for ordinary differential equations (65L20) Parallel numerical computation (65Y05) Numerical methods for initial value problems involving ordinary differential equations (65L05) Multistep, Runge-Kutta and extrapolation methods for ordinary differential equations (65L06) Multigrid methods; domain decomposition for initial value and initial-boundary value problems involving PDEs (65M55) Numerical integration (65D30) Acceleration of convergence in numerical analysis (65B99)

Related Items (4)

An adaptive parareal algorithm ⋮ Machine-learning error models for approximate solutions to parameterized systems of nonlinear equations ⋮ Space--Time Least-Squares Petrov--Galerkin Projection for Nonlinear Model Reduction ⋮ Projection-based model reduction of dynamical systems using space-time subspace and machine learning

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