Numerical solvers of partial differential equations (PDEs) have been widely employed for
simulating physical systems. However, the computational cost remains a major bottleneck in …

Enabling fast and accurate physical simulations with data has become an important area of
computational physics to aid in inverse problems, design-optimization, uncertainty …

The long runtime of high-fidelity partial differential equation (PDE) solvers makes them
unsuitable for time-critical applications. We propose to accelerate PDE solvers using …

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to
many engineering applications (eg, the process of CO 2 sequestration). Here, we extend …

This paper integrates nonlinear-manifold reduced order models (NM-ROMs) with domain
decomposition (DD). NM-ROMs approximate the full order model (FOM) state in a nonlinear …

Numerically solving partial differential equations (PDEs) can be challenging and
computationally expensive. This has led to the development of reduced-order models …

Abstract Dynamic Mode Decomposition (DMD) is a model-order reduction approach,
whereby spatial modes of fixed temporal frequencies are extracted from numerical or …

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification
(gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order …

A novel domain-decomposition least-squares Petrov–Galerkin (DD-LSPG) model-reduction
method applicable to parameterized systems of nonlinear algebraic equations (eg, arising …

While projection-based reduced order models can reduce the dimension of full order
solutions, the resulting reduced models may still contain terms that scale with the full order …