Bayesian operator inference for data-driven reduced-order modeling (Q2679294)

scientific article; zbMATH DE number 7643949

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English	Bayesian operator inference for data-driven reduced-order modeling	scientific article; zbMATH DE number 7643949

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instance of

scholarly article

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title

Bayesian operator inference for data-driven reduced-order modeling (English)

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Computer Methods in Applied Mechanics and Engineering

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publication date

19 January 2023

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full work available at URL

https://arxiv.org/abs/2204.10829

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zbMATH Keywords

data-driven reduced-order modeling

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uncertainty quantification

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operator inference

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Bayesian inversion

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Tikhonov regularization

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single-injector combustion

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describes a project that uses

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

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Reduced order methods for modeling and computational reduction

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Certified Reduced Basis Methods for Parametrized Partial Differential Equations

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Dynamic mode decomposition of numerical and experimental data

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

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

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Data-driven discovery of coordinates and governing equations

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Learning partial differential equations via data discovery and sparse optimization

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

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Data-driven reduced order modeling for time-dependent problems

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

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

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Bayesian solution uncertainty quantification for differential equations

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A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems

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Robust data-driven discovery of governing physical laws with error bars

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Verification and validation in computational engineering and science: basic concepts

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Estimation of modeling error in computational mechanics.

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Theory and methodology for estimation and control of errors due to modeling, approximation, and uncertainty

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Adaptive multiscale predictive modelling

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A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems

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Bayesian calibration, validation, and uncertainty quantification of diffuse interface models of tumor growth

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A computational framework for dynamic data-driven material damage control, based on Bayesian inference and model selection

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

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

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The ubiquitous Kronecker product

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

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Tensor Decompositions and Applications

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Q3394879

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On proper orthogonal decomposition of randomly perturbed fields with applications to flow past a cylinder and natural convection over a horizontal plate

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Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions

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Operator inference and physics-informed learning of low-dimensional models for incompressible flows

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Q4331455

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A Simplex Method for Function Minimization

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CMA.2022.115336

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Sitelinks

Mathematics(1 entry)

mardi Publication:2679294