On-the-fly construction of surrogate constitutive models for concurrent multiscale mechanical analysis through probabilistic machine learning
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Publication:6186258
DOI10.1016/j.jcpx.2020.100083arXiv2007.07749OpenAlexW3115358268MaRDI QIDQ6186258
Frans P. van der Meer, I. B. C. M. Rocha, Pierre Kerfriden
Publication date: 9 January 2024
Published in: Journal of Computational Physics: X (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2007.07749
Numerical and other methods in solid mechanics (74Sxx) Material properties given special treatment (74Exx) Homogenization, determination of effective properties in solid mechanics (74Qxx)
Cites Work
- Unnamed Item
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- Automatised selection of load paths to construct reduced-order models in computational damage micromechanics: from dissipation-driven random selection to Bayesian optimization
- A partitioned model order reduction approach to rationalise computational expenses in nonlinear fracture mechanics
- Bridging proper orthogonal decomposition methods and augmented Newton-Krylov algorithms: an adaptive model order reduction for highly nonlinear mechanical problems
- Wavelet based reduced order models for microstructural analyses
- A priori hyperreduction method: an adaptive approach
- Multi-scale computational homogenization: trends and challenges
- A phantom node formulation with mixed mode cohesive law for splitting in laminates
- Efficient micromechanical analysis of fiber-reinforced composites subjected to cyclic loading through time homogenization and reduced-order modeling
- A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
- An adaptive domain-based POD/ECM hyper-reduced modeling framework without offline training
- Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
- Micromechanics-based surrogate models for the response of composites: a critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks
- Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients
- Dimensional hyper-reduction of nonlinear finite element models via empirical cubature
- POD-DEIM model order reduction for strain-softening viscoplasticity
- Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials
- A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
- A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
- Failure-oriented multi-scale variational formulation: micro-structures with nucleation and evolution of softening bands
- A finite element method for the simulation of strong and weak discontinuities in solid mechanics
- Continuum approach to computational multiscale modeling of propagating fracture
- Computational homogenization for multiscale crack modeling. Implementational and computational aspects
- CutFEM: Discretizing geometry and partial differential equations
- Computational homogenization of nonlinear elastic materials using neural networks
- Two-scale diffusion-deformation coupling model for material deterioration involving micro-crack propagation
- Adaptive sampling in hierarchical simulation
- Computational homogenization for heat conduction in heterogeneous solids
- Least squares quantization in PCM
- Galerkin Proper Orthogonal Decomposition Methods for a General Equation in Fluid Dynamics
- INFERENCE AND UNCERTAINTY PROPAGATION OF ATOMISTICALLY INFORMED CONTINUUM CONSTITUTIVE LAWS, PART 2: GENERALIZED CONTINUUM MODELS BASED ON GAUSSIAN PROCESSES
- An approach to micro-macro modeling of heterogeneous materials
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