Weak SINDy for partial differential equations (Q2132599)

scientific article; zbMATH DE number 7515424

Language	Label	Description	Also known as
English	Weak SINDy for partial differential equations	scientific article; zbMATH DE number 7515424

Statements

instance of

scholarly article

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title

Weak SINDy for partial differential equations (English)

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Journal of Computational Physics

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publication date

28 April 2022

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full work available at URL

https://arxiv.org/abs/2007.02848

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zbMATH Keywords

data-driven model selection

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partial differential equations

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weak solutions

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sparse recovery

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Galerkin method

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convolution

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describes a project that uses

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A new look at the statistical model identification

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Model selection and mixed-effects modeling of HIV infection dynamics

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

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Sparse identification of nonlinear dynamical systems via reweighted \(\ell_1\)-regularized least squares

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Q3830265

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Detecting the maximum likelihood transition path from data of stochastic dynamical systems

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Robust and optimal sparse regression for nonlinear PDE models

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MADNESS: a multiresolution, adaptive numerical environment for scientific simulation

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Fourth-Order Time-Stepping for Stiff PDEs

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Generalized sensitivity functions for size-structured population models

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Discovery of Dynamics Using Linear Multistep Methods

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Optimal Detection of Changepoints With a Linear Computational Cost

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Learning partial differential equations for biological transport models from noisy spatio-temporal data

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PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network

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Model selection for dynamical systems via sparse regression and information criteria

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Weak SINDy: Galerkin-Based Data-Driven Model Selection

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Learning equations from biological data with limited time samples

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Bayesian Numerical Homogenization

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DIFFUSION FROM AN INSTANTANEOUS POINT SOURCE WITH A CONCENTRATION-DEPENDENT COEFFICIENT

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Efficient Computer Manipulation of Tensor Products with Applications to Multidimensional Approximation

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Data-Driven Learning of Nonautonomous Systems

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Data driven governing equations approximation using deep neural networks

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Machine learning of linear differential equations using Gaussian processes

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Deep learning of dynamics and signal-noise decomposition with time-stepping constraints

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Learning partial differential equations via data discovery and sparse optimization

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Extracting Sparse High-Dimensional Dynamics from Limited Data

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The immersed boundary method: a projection approach

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Sparse identification of truncation errors

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Deep multiscale model learning

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Variational system identification of the partial differential equations governing the physics of pattern-formation: inference under varying fidelity and noise

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Using experimental data and information criteria to guide model selection for reaction-diffusion problems in mathematical biology

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Numerical aspects for approximating governing equations using data

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Data-driven deep learning of partial differential equations in modal space

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DLGA-PDE: discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm

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On the Convergence of the SINDy Algorithm

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

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.JCP.2021.110525

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Sitelinks

Mathematics(1 entry)

mardi Publication:2132599