Nonintrusive Reduced-Order Models for Parametric Partial Differential Equations via Data-Driven Operator Inference (Q6175122)

scientific article; zbMATH DE number 7729490

Language	Label	Description	Also known as
English	Nonintrusive Reduced-Order Models for Parametric Partial Differential Equations via Data-Driven Operator Inference	scientific article; zbMATH DE number 7729490

Statements

instance of

scholarly article

0 references

title

Nonintrusive Reduced-Order Models for Parametric Partial Differential Equations via Data-Driven Operator Inference (English)

0 references

0 references

0 references

0 references

SIAM Journal on Scientific Computing

0 references

publication date

17 August 2023

0 references

full work available at URL

https://arxiv.org/abs/2110.07653

0 references

zbMATH Keywords

parametric model reduction

0 references

operator inference

0 references

scientific machine learning

0 references

data-driven reduced model

0 references

MaRDI profile type

MaRDI publication profile

0 references

cites work

On the optimality of the Karhunen-Loève expansion (Corresp.)

0 references

On the Scalar Rational Interpolation Problem

0 references

Model Reduction of Bilinear Systems in the Loewner Framework

0 references

Singular Hopf Bifurcation to Relaxation Oscillations

0 references

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

0 references

Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms

0 references

A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems

0 references

Model reduction and neural networks for parametric PDEs

0 references

Q4868585

0 references

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

0 references

Compressed sensing and dynamic mode decomposition

0 references

Nonlinear Model Reduction via Discrete Empirical Interpolation

0 references

Integration of Stiff Equations

0 references

Learning physics-based models from data: perspectives from inverse problems and model reduction

0 references

Q4917542

0 references

Optimal control of vortex shedding using low-order models. Part I?open-loop model development

0 references

<i>A posteriori</i>error bounds for reduced-basis approximations of parametrized parabolic partial differential equations

0 references

Analysis of Discrete Ill-Posed Problems by Means of the L-Curve

0 references

Data-Driven Parametrized Model Reduction in the Loewner Framework

0 references

Q5568891

0 references

Non-intrusive data-driven model reduction for differential-algebraic equations derived from lifting transformations

0 references

Tensor Decompositions and Applications

0 references

Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

0 references

Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems

0 references

A Simplex Method for Function Minimization

0 references

Tensor Methods for Nonlinear Matrix Completion

0 references

Sampling Low-Dimensional Markovian Dynamics for Preasymptotically Recovering Reduced Models from Data with Operator Inference

0 references

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

0 references

Reduced Operator Inference for Nonlinear Partial Differential Equations

0 references

Lift \& learn: physics-informed machine learning for large-scale nonlinear dynamical systems

0 references

Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations. Application to transport and continuum mechanics.

0 references

Data-Driven Identification of Parametric Partial Differential Equations

0 references

Learning partial differential equations via data discovery and sparse optimization

0 references

Dynamic mode decomposition of numerical and experimental data

0 references

Hamiltonian operator inference: physics-preserving learning of reduced-order models for canonical Hamiltonian systems

0 references

Turbulence and the dynamics of coherent structures. I. Coherent structures

0 references

Automatic control and adaptive time-stepping

0 references

Q4125663

0 references

Q4337625

0 references

Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations

0 references

The ubiquitous Kronecker product

0 references

Certified real‐time solution of the parametrized steady incompressible Navier–Stokes equations: rigorous reduced‐basis <i>a posteriori</i> error bounds

0 references

Q4714145

0 references

Identifiers

zbMATH Open document ID

0 references

0 references

0 references

Mathematics Subject Classification ID

0 references

0 references

0 references

0 references

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:6175122