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Seq-SVF: an unsupervised data-driven method for automatically identifying hidden governing equations - MaRDI portal

Seq-SVF: an unsupervised data-driven method for automatically identifying hidden governing equations (Q6051370)

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scientific article; zbMATH DE number 7740246

Language	Label	Description	Also known as
English	Seq-SVF: an unsupervised data-driven method for automatically identifying hidden governing equations	scientific article; zbMATH DE number 7740246

Statements

scholarly article

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Seq-SVF: an unsupervised data-driven method for automatically identifying hidden governing equations (English)

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Computer Physics Communications

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publication date

20 September 2023

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zbMATH Keywords

system identification

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data-driven method

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singular value decomposition

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sparse learning

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MaRDI profile type

MaRDI publication profile

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Machine learning: Trends, perspectives, and prospects

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Numerical implementation of a neural network based material model in finite element analysis

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Data-driven computational mechanics

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Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials

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A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality

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Data driven modeling of plastic deformation

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A manifold learning approach to data-driven computational elasticity and inelasticity

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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

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Automated reverse engineering of nonlinear dynamical systems

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

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Machine learning of linear differential equations using Gaussian processes

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Multilayer feedforward networks are universal approximators

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Approximation by superpositions of a sigmoidal function

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Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker--Planck Equation and Physics-Informed Neural Networks

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Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks

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Compressed sensing

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

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Finding a Sparse Vector in a Subspace: Linear Sparsity Using Alternating Directions

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On the Early History of the Singular Value Decomposition

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Literature survey on low rank approximation of matrices

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Randomized algorithms for the low-rank approximation of matrices

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Efficient Algorithms for Computing a Strong Rank-Revealing QR Factorization

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Analytical study of the thermal shock problem of a half-space with various thermoelastic models

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Deterministic Nonperiodic Flow

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Identifiers

zbMATH Open document ID

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Mathematics Subject Classification ID

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zbMATH DE Number

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10.1016/J.CPC.2023.108887

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Sitelinks

Mathematics(1 entry)

mardi Publication:6051370

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