Data|driven model reduction for stochastic Burgers equations

Data-Driven Model Reduction for Stochastic Burgers Equations - MDPI

We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, ...

Data-driven model reduction for stochastic Burgers equations - arXiv

Abstract:We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning ...

Data-driven model reduction for stochastic Burgers equations

Interested in: efficient simulations of (Cv1:K ), K << N. Question: a reduced closure model of (Cv1:K )? Space-time reduction: reduce spatial dimension + ...

Data-Driven Model Reduction for Stochastic Burgers Equations

We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the ...

(PDF) Data-driven Model Reduction for Stochastic Burgers Equations

PDF | We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map,.

[PDF] Data-Driven Model Reduction for Stochastic Burgers Equations

A potential criterion for optimal space-time reduction is reported: the NAR models achieve minimal relative error in the energy spectrum at the time step ...

Data-driven Model Reduction for Stochastic Burgers Equations

We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the ...

Data-Driven Model Reduction for Stochastic Burgers Equations - OUCI

We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, ...

Data-Driven Model Reduction for Stochastic Burgers Equations ...

Within the context of a viscous stochastic Burgers equation, we show that a data-driven reduced model, in the form of nonlinear autoregression (NAR) time series ...

Data-Driven Model Reduction for Stochastic Burgers Equations - HvA

We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, ...

Data-Driven Model Reduction for Stochastic Burgers Equations ...

We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, ...

Data-driven structure-preserving model reduction for stochastic ...

The equations governing the evolution of the system are then projected to that subspace, and in the online stage such a reduced model is solved ...

Data-driven structure-preserving model reduction for stochastic ...

The equations governing the evolution of the system are then projected to that subspace, and in the online stage such a reduced model is solved numerically at a ...

Award # 1821211 - Data-Driven Stochastic Model Reduction and Its ...

This project has developed methods and theories for stochastic model reduction from data for complex dynamics. The stochastic reduced models ...

Fei Lu's Homepage - Johns Hopkins University

We focus on the mathematical understanding of such inference-based data-driven approach for model reduction of complex dynamics. Discrete-time stochastic ...

An efficient data-driven multiscale stochastic reduced order ...

The statistical behavior of the solutions to the viscous stochastic Burgers equation (22) depends on the decay properties of the stochastic forcing coefficients ...

Shock trace prediction by reduced models for a viscous stochastic ...

Model reduction can thus play an essential role in reducing the computational cost for the prediction of shocks. Yet, reduced models typically aim to ...

regularized reduced order models for a stochastic burgers equation

In this paper, we focus on an SBE driven by linear multiplicative noise, which is presented briefly in Section 2.1. To fix ideas, the ROMs explored in this ...

Shock trace prediction by reduced models for a viscous stochastic ...

Within the context of a viscous stochastic Burgers equation, we show that a data-driven reduced model, in the form of nonlinear autoregression (NAR) time series.

Data-driven model reduction, Wiener projections, and the Koopman ...

Data-driven model reduction for deterministic and random dynamical systems. · A precise formulation of data-driven modeling based on discrete- ...