Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression (Q6048987)

scientific article; zbMATH DE number 7750303

Language	Label	Description	Also known as
English	Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression	scientific article; zbMATH DE number 7750303

Statements

instance of

scholarly article

0 references

title

Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression (English)

0 references

0 references

0 references

0 references

0 references

0 references

13 October 2023

0 references

full work available at URL

https://arxiv.org/abs/2302.08216

0 references

zbMATH Keywords

uncertainty quantification

0 references

reduced order modeling

0 references

Gaussian process regression

0 references

nonlinear solid mechanics

0 references

sensitivity analysis

0 references

parameter estimation

0 references

MaRDI profile type

Publication

0 references

cites work

Nonintrusive reduced-order modeling of parametrized time-dependent partial differential equations

0 references

A nonintrusive reduced order modelling approach using proper orthogonal decomposition and locally adaptive sparse grids

0 references

Nonlinear model order reduction based on local reduced-order bases

0 references

A survey of projection-based model reduction methods for parametric dynamical systems

0 references

An `empirical interpolation' method: Application to efficient reduced-basis discretization of partial differential equations

0 references

A matrix DEIM technique for model reduction of nonlinear parametrized problems in cardiac mechanics

0 references

Deep-HyROMnet: a deep learning-based operator approximation for hyper-reduction of nonlinear parametrized PDEs

0 references

Multi-fidelity surrogate modeling using long short-term memory networks

0 references

Data-driven model reduction for the Bayesian solution of inverse problems

0 references

Nonlinear Model Reduction via Discrete Empirical Interpolation

0 references

Reduced basis approximation for nonlinear parametrized evolution equations based on empirical operator interpolation

0 references

A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs

0 references

POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition

0 references

A deep learning approach to Reduced Order Modelling of parameter dependent partial differential equations

0 references

Reduced order modeling for nonlinear structural analysis using Gaussian process regression

0 references

Data-driven reduced order modeling for time-dependent problems

0 references

Sensitivity analysis for multidimensional and functional outputs

0 references

Non-Intrusive Reduced Order Modeling of Convection Dominated Flows Using Artificial Neural Networks with Application to Rayleigh-Taylor Instability

0 references

Multi-fidelity regression using artificial neural networks: efficient approximation of parameter-dependent output quantities

0 references

Bayesian operator inference for data-driven reduced-order modeling

0 references

POD-DEIM model order reduction for strain-softening viscoplasticity

0 references

Biomechanics of soft tissue in cardiovascular systems. Lecture notes for an advanced school on ``Biomechanics of soft tissue'', Udine, Italy, September 10--14, 2001

0 references

Reduced basis methods for time-dependent problems

0 references

Certified Reduced Basis Methods for Parametrized Partial Differential Equations

0 references

Non-intrusive reduced order modeling of nonlinear problems using neural networks

0 references

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

0 references

Adaptive construction of surrogates for the Bayesian solution of inverse problems

0 references

Model Order Reduction in Fluid Dynamics: Challenges and Perspectives

0 references

Machine learning. A probabilistic perspective

0 references

Efficient model reduction of parametrized systems by matrix discrete empirical interpolation

0 references

Data-driven operator inference for nonintrusive projection-based model reduction

0 references

Reduced Basis Methods for Partial Differential Equations

0 references

Multifidelity Monte Carlo Estimation of Variance and Sensitivity Indices

0 references

POD-based model reduction with empirical interpolation applied to nonlinear elasticity

0 references

Making best use of model evaluations to compute sensitivity indices

0 references

Non intrusive reduced order modeling of parametrized PDEs by kernel POD and neural networks

0 references

Projection-based model reduction: formulations for physics-based machine learning

0 references

On sensitivity estimation for nonlinear mathematical models

0 references

Reduced order modelling for unsteady fluid flow using proper orthogonal decomposition and radial basis functions

0 references

Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem

0 references

Q3394879

0 references

A parameterized non-intrusive reduced order model and error analysis for general time-dependent nonlinear partial differential equations and its applications

0 references

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

0 references

Model order reduction for large-scale structures with local nonlinearities

0 references

published in

Computers & Mathematics with Applications

0 references

Identifiers

arXiv ID

2302.08216

0 references

Mathematics Subject Classification ID

0 references

0 references

0 references

0 references

0 references

0 references

0 references

10.1016/J.CAMWA.2023.08.016

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:6048987