Surrogate modeling of elasto-plastic problems via long short-term memory neural networks and proper orthogonal decomposition
DOI10.1016/j.cma.2021.114030zbMath1502.74017OpenAlexW3187481252MaRDI QIDQ2237770
Sunyoung Im, Maenghyo Cho, Jonggeon Lee
Publication date: 28 October 2021
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2021.114030
elasto-plasticitysurrogate modelproper orthogonal decomposition (POD)long short-term memory (LSTM)nonlinear model order reduction (MOR)
Small-strain, rate-independent theories of plasticity (including rigid-plastic and elasto-plastic materials) (74C05) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30)
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- An effective computational tool for parametric studies and identification problems in materials mechanics
- Model reduction in elastoplasticity: proper orthogonal decomposition combined with adaptive sub-structuring
- Multi-level a priori hyper-reduction of mechanical models involving internal variables
- Equivalent model construction for a non-linear dynamic system based on an element-wise stiffness evaluation procedure and reduced analysis of the equivalent system
- Kriging metamodeling in simulation: a review
- Structural reliability analysis of elastic-plastic structures using neural networks and Monte Carlo simulation
- Metamodeling: Radial basis functions, versus polynomials
- Model order reduction and domain decomposition strategies for the solution of the dynamic elastic-plastic structural problem
- Machine learning aided stochastic elastoplastic analysis
- Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
- An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new time-distributed residual U-net architecture
- An artificial neural network framework for reduced order modeling of transient flows
- Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
- A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
- Reduction process based on proper orthogonal decomposition for dual formulation of dynamic substructures
- An interpolation-based parametric reduced order model combined with component mode synthesis
- A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
- Derivation of heterogeneous material laws via data-driven principal component expansions
- Two-level scheme for selection of primary degrees of freedom and semi-analytic sensitivity based on the reduced system
- Characterizing rate-dependent material behaviors in self-learning simulation
- Model order reduction for nonlinear dynamical systems based on trajectory piecewise-linear approximations
- PHYSICAL INTERPRETATION OF THE PROPER ORTHOGONAL MODES USING THE SINGULAR VALUE DECOMPOSITION
- Nonlinear model order reduction based on local reduced-order bases
- Computational homogenization of nonlinear elastic materials using neural networks
- Introduction to Nonlinear Finite Element Analysis
- An acceleration method in elasto-plastic finite element computation
- Construction of reduced-order models for fluid flows using deep feedforward neural networks
- Coupling of substructures for dynamic analyses.
- Metamodels for computer-based engineering design: Survey and recommendations
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