A reduced basis method based on a physics-informed machine learning framework is
developed for efficient reduced-order modeling of parametrized partial differential equations …

Numerical simulation of parametrized differential equations is of crucial importance in the
study of real-world phenomena in applied science and engineering. Computational methods …

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 …

An adaptive projection-based reduced-order model (ROM) formulation is presented for
model-order reduction of problems featuring chaotic and convection-dominant physics. An …

This work focuses on the conservation of quantities such as Hamiltonians, mass, and
momentum when solution fields of partial differential equations are approximated with …

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 …

Reduced order models of nonlinear conservation laws in fluid dynamics do not typically
inherit stability properties of the full order model. We introduce projection-based hyper …

Computationally efficient modeling of gas turbine combustion is challenging due to the
chaotic multi-scale physics and the complex non-linear interactions between acoustic …

We discuss the recent developments of projection-based model order reduction (MOR)
techniques targeting Hamiltonian problems. Hamilton's principle completely characterizes …

A novel hyper-reduction method is proposed that conserves kinetic energy and momentum
for reduced order models of the incompressible Navier-Stokes equations. The main …