An Adaptive Model Reduction Method Leveraging Locally Supported Basis Functions DOI
Han Gao, Matthew J. Zahr

International journal of computational fluid dynamics, Journal Year: 2023, Volume and Issue: 37(6), P. 451 - 473

Published: July 3, 2023

Language: Английский

The role of interface boundary conditions and sampling strategies for Schwarz-based coupling of projection-based reduced order models DOI
Christopher R. Wentland, Francesco Rizzi,

Joshua Barnett

et al.

Journal of Computational and Applied Mathematics, Journal Year: 2025, Volume and Issue: unknown, P. 116584 - 116584

Published: Feb. 1, 2025

Language: Английский

Citations

1
A cross algorithm for implicit time integration of random partial differential equations on low-rank matrix manifolds DOI
M. H. Naderi, S. Akhavan, Hessam Babaee

et al.

Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences, Journal Year: 2025, Volume and Issue: 481(2309)

Published: March 1, 2025

Dynamical low-rank approximation allows for solving large-scale matrix differential equations (MDEs) with significantly fewer degrees of freedom and has been applied to a growing number applications. However, most existing techniques rely on explicit time integration schemes. In this work, we introduce cost-effective Newton’s method the implicit stiff, nonlinear MDEs manifolds. Our methodology is focused resulting from discretization random partial (PDEs), where columns MDE can be solved independently. Cost-effectiveness achieved by at minimum entries required rank- r approximation. We present novel cross that requires parametric PDE strategically selected parameters O ( stretchy="false">) grid points using method. The samples adaptively vary over are chosen discrete empirical interpolation or similar techniques. proposed developed high-order multi-step Runge–Kutta schemes incorporates rank adaptivity, allowing dynamic adjustment control error. Several analytical examples, including stochastic Burgers’ Gray–Scott equations, demonstrate accuracy efficiency presented methodology.

Language: Английский

Citations

1
A non-overlapping optimization-based domain decomposition approach to component-based model reduction of incompressible flows DOI
Tommaso Taddei,

Xuejun Xu,

Lei Zhang

et al.

Journal of Computational Physics, Journal Year: 2024, Volume and Issue: 509, P. 113038 - 113038

Published: April 23, 2024

Language: Английский

Citations

6
A multifidelity deep operator network approach to closure for multiscale systems DOI Creative Commons
Shady E. Ahmed, Panos Stinis

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2023, Volume and Issue: 414, P. 116161 - 116161

Published: June 21, 2023

Language: Английский

Citations

11
Explicit synchronous partitioned scheme for coupled reduced order models based on composite reduced bases DOI Creative Commons
Amy de Castro, Pavel Bochev, Paul Kuberry

et al.

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2023, Volume and Issue: 417, P. 116398 - 116398

Published: Sept. 25, 2023

Language: Английский

Citations

11
Accelerating high-fidelity simulations of chemically reacting flows using reduced-order modeling with time-dependent bases DOI

Ki Sung Jung,

Cristian E. Lacey, Hessam Babaee

et al.

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 437, P. 117758 - 117758

Published: Feb. 1, 2025

Language: Английский

Citations

0
Component Based Reduced Order Modelling of Two Dimensional Rotating Detonation Engine With Non-Uniform Injection DOI

Ryan G. Camacho,

Cheng Huang

AIAA SCITECH 2022 Forum, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

Citations

0
Information Theoretic Clustering for Coarse-grained Modeling of Non-equilibrium Gas Dynamics DOI Creative Commons
Christian S. Jacobsen, Ivan Zanardi, Sahil Bhola

et al.

Journal of Computational Physics, Journal Year: 2024, Volume and Issue: 507, P. 112977 - 112977

Published: March 28, 2024

Language: Английский

Citations

3
Domain Decomposition for Data-Driven Reduced Modeling of Large-Scale Systems DOI
Ionuţ-Gabriel Farcaş,

Rayomand P. Gundevia,

Ramakanth Munipalli

et al.

AIAA Journal, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 16

Published: Sept. 25, 2024

This paper focuses on the construction of accurate and predictive data-driven reduced models large-scale numerical simulations with complex dynamics sparse training datasets. In these settings, standard, single-domain approaches may be too inaccurate or overfit hence generalize poorly. Moreover, processing datasets typically requires significant memory computing resources, which can render computationally prohibitive. To address challenges, we introduce a domain-decomposition formulation into model. doing so, basis functions used in model approximation become localized space, increase accuracy domain-decomposed dynamics. The decomposition furthermore reduces requirements to process underlying dataset. We demonstrate effectiveness scalability our approach three-dimensional unsteady rotating-detonation rocket engine simulation scenario more than 75 million degrees freedom Our results show that compared approach, version both prediction errors for pressure by up 13% 5% other key quantities, such as temperature, fuel, oxidizer mass fractions. Lastly, decreases almost factor four, turn well.

Language: Английский

Citations

3
Reduced Order Modeling of Turbulent Reacting Flows on Low-Rank Matrix Manifolds DOI Creative Commons
Aidyn Aitzhan, Arash G. Nouri, Peyman Givi

et al.

Journal of Computational Physics, Journal Year: 2024, Volume and Issue: unknown, P. 113549 - 113549

Published: Oct. 1, 2024

Language: Английский

Citations

2