Data|driven model reduction for stochastic Burgers equations

The algorithm is demonstrated on a semi-discretized partial differential equation, namely Burgers equation, which illustrates that higher-degree transformations ...

Ilya Timofeyev Publications

Publications. Most Recent. Robert Azencott, Brett Geiger, Ilya Timofeyev "Rare Events Analysis in Stochastic Models for Bacterial Evolution", submitted to ...

Exact solutions of stochastic Burgers–Korteweg de Vries type ...

Our discovery indicates that solving certain deterministic counterparts of KdV–Burgers equations and composing the solution with a solution of ...

Statistical analysis and simulation of random shocks in stochastic ...

We study the statistical properties of random shock waves in stochastic Burgers equation subject to random space–time perturbations and ...

Data-driven discovery of partial differential equations - Science

The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can ...

Numerical analysis of Burgers' equation with uncertain boundary ...

stochastic Galerkin method. Per Pettersson∗, Gianluca Iaccarino ... The latter kind of uncertainty can be reduced by more accurate measurements ...

Data-driven discovery of partial differential equations

Crommelin, Normal forms for reduced stochastic climate models. Proc. Natl. Acad. Sci. U.S.A. 106, 3649–3653 (2009). 8. D. Giannakis A. J. ...

An Adaptive ANOVA-Based Data-Driven Stochastic Method for ...

An adaptive ANOVA strategy is also provided to further reduce the number of the stochastic subproblems and speed up our method. To demonstrate the accuracy and ...

Yuri Bakhtin: Ergodic theory of the stochastic Burgers equation

... Burgers equation, which is a basic evolutionary stochastic PDE of Hamilton-Jacobi type related to fluid dynamics, growth models, and the KPZ ...

Evolve Then Filter Regularization for Stochastic Reduced Order ...

Our numerical results based on a stochastic Burgers equation with linear multiplicative noise. It shows that the EF-ROM is significantly better ...

Non-intrusive Data-driven Model Reduction for Differential Algebraic ...

The reduced operators for the differential equations are inferred from lifted snapshot data using operator inference, which solves a linear ...

arXiv:1709.04362v1 [physics.flu-dyn] 11 Sep 2017 - VTechWorks

Abstract. We propose a data-driven filtered reduced order model (DDF-ROM) framework for the numerical simulation of fluid flows.

Model Reduction of the Coupled Burgers Equation in ... - Boris Kramer

the POD models for the coupled Burgers equation and use the GFE to generate data. A comparison of the GFE and POD will be given. Finally ...

Remarkable statistical behavior for truncated Burgers–Hopf dynamics

to utilize this model to check reduced stochastic modeling ... (2000) Statistical Mechanics for Truncations of the. Burgers–Hopf Equation: A Model for Intrinsic ...

Learning data-driven discretizations for partial differential equations

But solutions to Burger's equations are not polynomials: They are shocks with characteristic properties. By using this information, we can ...

Large deviations for a stochastic Burgers' equation

stochastic Burgers' equation driven by a Brownian sheet, and prove the existence and uniqueness of global solutions in time, as well as the ...

Data-based stochastic model reduction for the Kuramoto ...

The problem of constructing data-based, predictive, reduced models for the Kuramoto–Sivashinsky equation is considered, under circumstances where one has ...

Low-Order Stochastic Mode Reduction for a Realistic Barotropic ...

Abstract This study applies a systematic strategy for stochastic modeling of atmospheric low-frequency variability to a realistic barotropic model climate.

Data-based stochastic model reduction for the Kuramoto ... - OSTI.GOV

In this paper, the problem of constructing data-based, predictive, reduced models for the Kuramoto–Sivashinsky equation is considered, under circumstances ...

Stochastic 3D Burgers equations with random initial data

In this talk, we discuss stochastic 3D Burgers equations driven by multiplicative noise, with uncertainty occurring in initial conditions as well.