A fast and accurate domain decomposition nonlinear manifold reduced order model
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 …
decomposition (DD). NM-ROMs approximate the full order model (FOM) state in a nonlinear …
SVD perspectives for augmenting DeepONet flexibility and interpretability
Deep operator networks (DeepONets) are powerful and flexible architectures that are
attracting attention in multiple fields due to their utility for fast and accurate emulation of …
attracting attention in multiple fields due to their utility for fast and accurate emulation of …
Gplasdi: Gaussian process-based interpretable latent space dynamics identification through deep autoencoder
Numerically solving partial differential equations (PDEs) can be challenging and
computationally expensive. This has led to the development of reduced-order models …
computationally expensive. This has led to the development of reduced-order models …
gLaSDI: Parametric physics-informed greedy latent space dynamics identification
A parametric adaptive physics-informed greedy Latent Space Dynamics Identification
(gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order …
(gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order …
Local Lagrangian reduced-order modeling for the Rayleigh-Taylor instability by solution manifold decomposition
Abstract The Rayleigh-Taylor instability is a classical hydrodynamic instability of great
interest in various disciplines of science and engineering, including astrophysics …
interest in various disciplines of science and engineering, including astrophysics …
Damage identification in fiber metal laminates using Bayesian analysis with model order reduction
NKB Muralidhar, C Gräßle, N Rauter… - Computer Methods in …, 2023 - Elsevier
Fiber metal laminates (FML) are composite structures consisting of metals and fiber
reinforced plastics (FRP) which have experienced an increasing interest as the choice of …
reinforced plastics (FRP) which have experienced an increasing interest as the choice of …
Accelerating kinetic simulations of electrostatic plasmas with reduced-order modeling
Despite the advancements in high-performance computing and modern numerical
algorithms, the cost remains prohibitive for multi-query kinetic plasma simulations. In this …
algorithms, the cost remains prohibitive for multi-query kinetic plasma simulations. In this …
Train small, model big: Scalable physics simulators via reduced order modeling and domain decomposition
Numerous cutting-edge scientific technologies originate at the laboratory scale, but
transitioning them to practical industry applications is a formidable challenge. Traditional …
transitioning them to practical industry applications is a formidable challenge. Traditional …
Reduced-order modeling for parameterized PDEs via implicit neural representations
We present a new data-driven reduced-order modeling approach to efficiently solve
parametrized partial differential equations (PDEs) for many-query problems. This work is …
parametrized partial differential equations (PDEs) for many-query problems. This work is …
Explicable hyper-reduced order models on nonlinearly approximated solution manifolds of compressible and incompressible Navier-Stokes equations
A slow decaying Kolmogorov n-width of the solution manifold of a parametric partial
differential equation precludes the realization of efficient linear projection-based reduced …
differential equation precludes the realization of efficient linear projection-based reduced …