Physics-informed deep neural operator networks
Standard neural networks can approximate general nonlinear operators, represented either
explicitly by a combination of mathematical operators, eg in an advection–diffusion reaction …
explicitly by a combination of mathematical operators, eg in an advection–diffusion reaction …
A one-shot overlap** Schwarz method for component-based model reduction: application to nonlinear elasticity
We propose a component-based (CB) parametric model order reduction (pMOR) formulation
for parameterized nonlinear elliptic partial differential equations (PDEs) based on …
for parameterized nonlinear elliptic partial differential equations (PDEs) based on …
The Schwarz alternating method for the seamless coupling of nonlinear reduced order models and full order models
J Barnett, I Tezaur, A Mota - ar** of coherent structures in parameterized flows by learning optimal transportation with Gaussian models
We present a general (ie, independent of the underlying model) interpolation technique
based on optimal transportation of Gaussian models for parametric advection-dominated …
based on optimal transportation of Gaussian models for parametric advection-dominated …
Efficient hyperreduction of high-order discontinuous Galerkin methods: element-wise and point-wise reduced quadrature formulations
E Du, M Yano - Journal of Computational Physics, 2022 - Elsevier
We develop and assess projection-based model reduction methods for high-order
discontinuous Galerkin (DG) discretizations of parametrized nonlinear partial differential …
discontinuous Galerkin (DG) discretizations of parametrized nonlinear partial differential …
An adaptive reduced order model for the angular discretization of the Boltzmann transport equation using independent basis sets over a partitioning of the space …
AC Hughes, AG Buchan - International Journal for Numerical …, 2022 - Wiley Online Library
This article presents a new reduced order model (ROM) for the angular discretization of the
Boltzmann transport equation. The angular ROM is built over a partitioning of the space …
Boltzmann transport equation. The angular ROM is built over a partitioning of the space …
Space‐local reduced‐order bases for accelerating reduced‐order models through sparsity
Projection‐based model order reduction (PMOR) methods based on linear or affine
approximation subspaces accelerate numerical predictions by reducing the dimensionality …
approximation subspaces accelerate numerical predictions by reducing the dimensionality …
[PDF][PDF] Fourier collocation and reduced basis methods for fast modeling of compressible flows
J Yu, D Ray, JS Hesthaven - Commun. Comput. Phys., 2022 - researchgate.net
A projection-based reduced order model (ROM) based on the Fourier collocation method is
proposed for compressible flows. The incorporation of localized artificial viscosity model and …
proposed for compressible flows. The incorporation of localized artificial viscosity model and …