Mori-Zwanzig reduced models for uncertainty quantification

DOI10.3934/jcd.2019002OpenAlexW2963418939MaRDI QIDQ2323351

Jing Li, Panos Stinis

Publication date: 30 August 2019

Published in: Journal of Computational Dynamics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1803.02826

zbMATH Keywords

renormalizationmodel reductionlong memoryuncertainty quantificationMori-Zwanzig formalismMarkovian reformulation

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Approximation by polynomials (41A10) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99)

Related Items (6)

The adjoint Petrov-Galerkin method for non-linear model reduction ⋮ Stochastic discontinuous Galerkin methods (SDGM) based on fluctuation-dissipation balance ⋮ Estimating the uncertainty in underresolved nonlinear dynamics ⋮ Kernel-based prediction of non-Markovian time series ⋮ Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism ⋮ Model reduction for a power grid model

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