Data-driven modeling for unsteady aerodynamics and aeroelasticity

J Kou, W Zhang - Progress in Aerospace Sciences, 2021 - Elsevier
Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

L Sun, H Gao, S Pan, JX Wang - Computer Methods in Applied Mechanics …, 2020 - Elsevier
Numerical simulations on fluid dynamics problems primarily rely on spatially or/and
temporally discretization of the governing equation using polynomials into a finite …

[HTML][HTML] Physics-informed machine learning for reduced-order modeling of nonlinear problems

W Chen, Q Wang, JS Hesthaven, C Zhang - Journal of computational …, 2021 - Elsevier
A reduced basis method based on a physics-informed machine learning framework is
developed for efficient reduced-order modeling of parametrized partial differential equations …

Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

Q Wang, N Ripamonti, JS Hesthaven - Journal of Computational Physics, 2020 - Elsevier
Closure modeling based on the Mori-Zwanzig formalism has proven effective to improve the
stability and accuracy of projection-based model order reduction. However, closure models …

Learning physics-based reduced-order models for a single-injector combustion process

R Swischuk, B Kramer, C Huang, K Willcox - AIAA Journal, 2020 - arc.aiaa.org
This paper presents a physics-based data-driven method to learn predictive reduced-order
models (ROMs) from high-fidelity simulations and illustrates it in the challenging context of a …

Bayesian operator inference for data-driven reduced-order modeling

M Guo, SA McQuarrie, KE Willcox - Computer Methods in Applied …, 2022 - Elsevier
This work proposes a Bayesian inference method for the reduced-order modeling of time-
dependent systems. Informed by the structure of the governing equations, the task of …

Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces

S Pan, N Arnold-Medabalimi… - Journal of Fluid …, 2021 - cambridge.org
Koopman decomposition is a nonlinear generalization of eigen-decomposition, and is being
increasingly utilized in the analysis of spatio-temporal dynamics. Well-known techniques …

The adjoint Petrov–Galerkin method for non-linear model reduction

EJ Parish, CR Wentland, K Duraisamy - Computer Methods in Applied …, 2020 - Elsevier
We formulate a new projection-based reduced-order modeling technique for non-linear
dynamical systems. The proposed technique, which we refer to as the Adjoint Petrov …

Front transport reduction for complex moving fronts: Nonlinear model reduction for an advection–reaction–diffusion equation with a Kolmogorov–Petrovsky–Piskunov …

P Krah, S Büchholz, M Häringer, J Reiss - Journal of scientific computing, 2023 - Springer
This work addresses model order reduction for complex moving fronts, which are
transported by advection or through a reaction–diffusion process. Such systems are …

Investigations and improvement of robustness of reduced-order models of reacting flow

C Huang, K Duraisamy, CL Merkle - AIAA Journal, 2019 - arc.aiaa.org
The impact of chemical reactions on the robustness and accuracy of projection-based
reduced-order models (ROMs) of fluid flows is investigated. Both Galerkin and least squares …