Extended tensor decomposition model reduction methods: training, prediction, and design under uncertainty
From MaRDI portal
Publication:6120135
DOI10.1016/j.cma.2023.116550arXiv2307.15873OpenAlexW4388439150MaRDI QIDQ6120135
Ye Lu, Satyajit Mojumder, Yangfan Li, Jiachen Guo, Wing Kam Liu
Publication date: 25 March 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2307.15873
additive manufacturingmulti-scale modelingnonlinear model reductionextended tensor decompositionXTD-SCA
Cites Work
- Tensor Decompositions and Applications
- Automatised selection of load paths to construct reduced-order models in computational damage micromechanics: from dissipation-driven random selection to Bayesian optimization
- The GNAT method for nonlinear model reduction: effective implementation and application to computational fluid dynamics and turbulent flows
- Multiparametric analysis within the proper generalized decomposition framework
- Ideal minimal residual-based proper generalized decomposition for non-symmetric multi-field models -- application to transient elastodynamics in space-time domain
- PGD-based \textit{computational vademecum} for efficient design, optimization and control
- Bridging proper orthogonal decomposition methods and augmented Newton-Krylov algorithms: an adaptive model order reduction for highly nonlinear mechanical problems
- The LATIN multiscale computational method and the proper generalized decomposition
- A new family of solvers for some classes of multidimensional partial differential equations encountered in kinetic theory modeling of complex fluids
- Some additional results on principal components analysis of three-mode data by means of alternating least squares algorithms
- Multilayer feedforward networks are universal approximators
- A numerical method for computing the overall response of nonlinear composites with complex microstructure
- A model reduction technique based on the PGD for elastic-viscoplastic computational analysis
- A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
- Datadriven HOPGD based computational vademecum for welding parameter identification
- Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering
- Adaptive hyper reduction for additive manufacturing thermal fluid analysis
- Hierarchical deep-learning neural networks: finite elements and beyond
- Multiresolution clustering analysis for efficient modeling of hierarchical material systems
- Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
- Proper generalized decomposition for parameterized Helmholtz problems in heterogeneous and unbounded domains: application to harbor agitation
- Rank-One Approximation to High Order Tensors
- Hyper-reduction of mechanical models involving internal variables
- Extended-PGD Model Reduction for Nonlinear Solid Mechanics Problems Involving Many Parameters
