Results 11 to 20 of about 7,720,158 (325)

Bayesian operator inference for data-driven reduced-order modeling [PDF]

Computer Methods in Applied Mechanics and Engineering, 2022
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 learning a reduced-order model from data is posed as a Bayesian inverse problem ...
Mengwu Guo, Shane A. McQuarrie, K. Willcox   +2 more
semanticscholar   +3 more sources

Physics-informed machine learning for reduced-order modeling of nonlinear problems

Journal of Computational Physics, 2021
A reduced basis method based on a physics-informed machine learning framework is developed for efficient reduced-order modeling of parametrized partial differential equations (PDEs).
Wenqian Chen, Qian Wang, J. Hesthaven, Chuhua Zhang   +3 more
semanticscholar   +3 more sources

Geometry Reduced Order Modeling (GROM) with application to modeling of glymphatic function

Brain Research Bulletin
Computational modeling of the brain has become a key part of understanding how the brain clears metabolic waste, but patient-specific modeling on a significant scale is still out of reach with current methods. We introduce a novel approach for leveraging
Andreas Solheim, Geir Ringstad, Per Kristian Eide, Kent-Andre Mardal   +3 more
doaj   +2 more sources

Reduced-Order modeling for Heston stochastic volatility model [PDF]

Hacettepe Journal of Mathematics and Statistics, 2016
In this paper, we compare the intrusive proper orthogonal decomposition (POD) with Galerkin projection and the data-driven dynamic mode decomposition (DMD), for Heston's option pricing model. The full order model is obtained by discontinuous Galerkin discretization in space and backward Euler in time.
Sinem Kozpınar, Murat Uzunca, Bülent Karasözen   +2 more
openaire   +6 more sources

Data-driven reduced order modeling for time-dependent problems

Computer Methods in Applied Mechanics and Engineering, 2019
A data-driven reduced basis (RB) method for parametrized time-dependent problems is proposed. This method requires the offline preparation of a database comprising the time history of the full-order solutions at parameter locations.
Mengwu Guo, J. Hesthaven
semanticscholar   +3 more sources

Reduced order modeling strategies for computational multiscale fracture [PDF]

Computer Methods in Applied Mechanics and Engineering, 2017
The paper proposes some new computational strategies for affordably solving multiscale fracture problems through a FE2 approach. To take into account the mechanical effects induced by fracture at the microstructure level the Representative Volume Element
Caicedo Silva, Manuel Alejandro, Hernández Ortega, Joaquín Alberto, Huespe, Alfredo Edmundo, Oliver Olivella, Xavier, Roubin, Emmanuel   +4 more
core   +4 more sources

Progressive transfer learning for advancing machine learning-based reduced-order modeling [PDF]

Scientific Reports
To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed.
Teeratorn Kadeethum, Daniel O’Malley, Youngsoo Choi, Hari S. Viswanathan, Hongkyu Yoon   +4 more
doaj   +2 more sources

On reduced-order modeling of drug dispersion in the spinal canal [PDF]

Fluids and Barriers of the CNS
The optimization of intrathecal drug delivery procedures requires a deeper understanding of flow and transport in the spinal canal. Numerical modeling of drug dispersion is challenging due to the disparity in time scales: dispersion occurs over 1 hour ...
F. J. Parras-Martos, A. L. Sánchez, C. Martínez-Bazán, W. Coenen, C. Gutiérrez-Montes   +4 more
doaj   +2 more sources

Inverse Reduced-Order Modeling [PDF]

, 2015
We propose a general probabilistic formulation of reduced-order modeling in the case the system state is hidden and characterized by some uncertainty. The objective is to integrate noisy and incomplete observations in the process of building a reduced-order model. We call this problematic inverse reduced-order modeling.
Héas, Patrick, Herzet, Cédric
openaire   +4 more sources

CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations [PDF]

International Conference on Learning Representations, 2022
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 reduced-order modeling (ROM).
Peter Yichen Chen, Jinxu Xiang, D. Cho, Yue Chang, G. Pershing, H. Maia, M. Chiaramonte, K. Carlberg, E. Grinspun   +8 more
semanticscholar   +1 more source