A kernel method for learning constitutive relation in data-driven computational elasticity
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Publication:2024599
DOI10.1007/s13160-020-00423-1zbMath1466.90099OpenAlexW3024517896MaRDI QIDQ2024599
Publication date: 4 May 2021
Published in: Japan Journal of Industrial and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s13160-020-00423-1
kernel methodmanifold learningregularized least squaresdata-driven computingmodel-free computational mechanics
Nonlinear programming (90C30) Numerical methods based on nonlinear programming (49M37) Least squares and related methods for stochastic control systems (93E24) Eigenvalues, singular values, and eigenvectors (15A18)
Related Items (6)
Accelerating the distance-minimizing method for data-driven elasticity with adaptive hyperparameters ⋮ Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials ⋮ JAX-FEM: a differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science ⋮ Computation‐with‐confidence for static elasticity: Data‐driven approach with order statistics ⋮ Distance-preserving manifold denoising for data-driven mechanics ⋮ Phase distribution and properties identification of heterogeneous materials: a data-driven approach
Uses Software
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