Numerical simulations on fluid dynamics problems primarily rely on spatially or/and temporally discretization of the governing equation using polynomials into a finite …
A reduced basis method based on a physics-informed machine learning framework is developed for efficient reduced-order modeling of parametrized partial differential equations …
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 …
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 …
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 …
Koopman decomposition is a nonlinear generalization of eigen-decomposition, and is being increasingly utilized in the analysis of spatio-temporal dynamics. Well-known techniques …
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 …
This work addresses model order reduction for complex moving fronts, which are transported by advection or through a reaction–diffusion process. Such systems are …
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 …